| Records |
| Author |
Farhan Riaz; Fernando Vilariño; Mario Dinis-Ribeiro; Miguel Coimbraln |
| Title |
Identifying Potentially Cancerous Tissues in Chromoendoscopy Images |
Type |
Conference Article |
| Year |
2011 |
Publication |
5th Iberian Conference on Pattern Recognition and Image Analysis |
Abbreviated Journal |
|
| Volume |
6669 |
Issue |
|
Pages |
709-716 |
| Keywords |
Endoscopy, Computer Assisted Diagnosis, Gradient. |
Abstract  |
The dynamics of image acquisition conditions for gastroenterology imaging scenarios pose novel challenges for automatic computer assisted decision systems. Such systems should have the ability to mimic the tissue characterization of the physicians. In this paper, our objective is to compare some feature extraction methods to classify a Chromoendoscopy image into two different classes: Normal and Potentially cancerous. Results show that LoG filters generally give best classification accuracy among the other feature extraction methods considered. |
| Address |
Las Palmas de Gran Canaria. Spain |
| Corporate Author |
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Thesis |
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| Publisher |
Springer |
Place of Publication |
Berlin |
Editor |
J. Vitria, J.M. Sanches, and M. Hernandez |
| Language |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
LNCS |
| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
|
ISBN |
978-3-642-21256-7 |
Medium |
|
| Area |
800 |
Expedition |
|
Conference |
IbPRIA |
| Notes |
MV;SIAI |
Approved |
no |
| Call Number |
Admin @ si @ RVD2011; IAM @ iam @ RVD2011 |
Serial |
1726 |
| Permanent link to this record |
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| |
| Author |
Joost Van de Weijer; Shida Beigpour |
| Title |
The Dichromatic Reflection Model: Future Research Directions and Applications |
Type |
Conference Article |
| Year |
2011 |
Publication |
International Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
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| Keywords |
dblp |
| Abstract |
The dichromatic reflection model (DRM) predicts that color distributions form a parallelogram in color space, whose shape is defined by the body reflectance and the illuminant color. In this paper we resume the assumptions which led to the DRM and shortly recall two of its main applications domains: color image segmentation and photometric invariant feature computation. After having introduced the model we discuss several limitations of the theory, especially those which are raised once working on real-world uncalibrated images. In addition, we summerize recent extensions of the model which allow to handle more complicated light interactions. Finally, we suggest some future research directions which would further extend its applicability. |
| Address |
Algarve, Portugal |
| Corporate Author |
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Thesis |
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| Publisher |
SciTePress |
Place of Publication |
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Editor |
Mestetskiy, Leonid and Braz, José |
| Language |
|
Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
|
ISBN |
978-989-8425-47-8 |
Medium |
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| Area |
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Expedition |
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Conference |
VISIGRAPP |
| Notes |
CIC |
Approved |
no |
| Call Number |
Admin @ si @ WeB2011 |
Serial |
1778 |
| Permanent link to this record |
| |
|
| |
| Author |
Miguel Oliveira; Angel Sappa; V.Santos |
| Title |
Unsupervised Local Color Correction for Coarsely Registered Images |
Type |
Conference Article |
| Year |
2011 |
Publication |
IEEE conference on Computer Vision and Pattern Recognition |
Abbreviated Journal |
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| Volume |
|
Issue |
|
Pages |
201-208 |
| Keywords |
|
| Abstract |
The current paper proposes a new parametric local color correction technique. Initially, several color transfer functions are computed from the output of the mean shift color segmentation algorithm. Secondly, color influence maps are calculated. Finally, the contribution of every color transfer function is merged using the weights from the color influence maps. The proposed approach is compared with both global and local color correction approaches. Results show that our method outperforms the technique ranked first in a recent performance evaluation on this topic. Moreover, the proposed approach is computed in about one tenth of the time. |
| Address |
Colorado Springs |
| Corporate Author |
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Thesis |
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| Publisher |
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Place of Publication |
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Editor |
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| Language |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
|
Edition |
|
| ISSN |
1063-6919 |
ISBN |
978-1-4577-0394-2 |
Medium |
|
| Area |
|
Expedition |
|
Conference |
CVPR |
| Notes |
ADAS |
Approved |
no |
| Call Number |
Admin @ si @ OSS2011; ADAS @ adas @ |
Serial |
1766 |
| Permanent link to this record |
| |
|
| |
| Author |
Miguel Angel Bautista; Sergio Escalera; Xavier Baro; Oriol Pujol; Jordi Vitria; Petia Radeva |
| Title |
On the Design of Low Redundancy Error-Correcting Output Codes |
Type |
Book Chapter |
| Year |
2011 |
Publication |
Ensembles in Machine Learning Applications |
Abbreviated Journal |
|
| Volume |
373 |
Issue |
2 |
Pages |
21-38 |
| Keywords |
|
| Abstract |
The classification of large number of object categories is a challenging trend in the Pattern Recognition field. In the literature, this is often addressed using an ensemble of classifiers . In this scope, the Error-Correcting Output Codes framework has demonstrated to be a powerful tool for combining classifiers. However, most of the state-of-the-art ECOC approaches use a linear or exponential number of classifiers, making the discrimination of a large number of classes unfeasible. In this paper, we explore and propose a compact design of ECOC in terms of the number of classifiers. Evolutionary computation is used for tuning the parameters of the classifiers and looking for the best compact ECOC code configuration. The results over several public UCI data sets and different multi-class Computer Vision problems show that the proposed methodology obtains comparable (even better) results than the state-of-the-art ECOC methodologies with far less number of dichotomizers. |
| Address |
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| Corporate Author |
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Thesis |
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| Publisher |
Springer Berlin Heidelberg |
Place of Publication |
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Editor |
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| Language |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
1860-949X |
ISBN |
978-3-642-22909-1 |
Medium |
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| Area |
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Expedition |
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Conference |
|
| Notes |
MILAB; OR;HuPBA;MV |
Approved |
no |
| Call Number |
Admin @ si @ BEB2011b |
Serial |
1886 |
| Permanent link to this record |
| |
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| |
| Author |
Alicia Fornes; Volkmar Frinken; Andreas Fischer; Jon Almazan; G. Jackson; Horst Bunke |
| Title |
A Keyword Spotting Approach Using Blurred Shape Model-Based Descriptors |
Type |
Conference Article |
| Year |
2011 |
Publication |
Proceedings of the 2011 Workshop on Historical Document Imaging and Processing |
Abbreviated Journal |
|
| Volume |
|
Issue |
|
Pages |
83-90 |
| Keywords |
|
| Abstract |
The automatic processing of handwritten historical documents is considered a hard problem in pattern recognition. In addition to the challenges given by modern handwritten data, a lack of training data as well as effects caused by the degradation of documents can be observed. In this scenario, keyword spotting arises to be a viable solution to make documents amenable for searching and browsing. For this task we propose the adaptation of shape descriptors used in symbol recognition. By treating each word image as a shape, it can be represented using the Blurred Shape Model and the De-formable Blurred Shape Model. Experiments on the George Washington database demonstrate that this approach is able to outperform the commonly used Dynamic Time Warping approach. |
| Address |
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| Corporate Author |
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Thesis |
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| Publisher |
ACM |
Place of Publication |
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Editor |
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| Language |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
|
ISBN |
978-1-4503-0916-5 |
Medium |
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| Area |
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Expedition |
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Conference |
HIP |
| Notes |
DAG |
Approved |
no |
| Call Number |
Admin @ si @ FFF2011a |
Serial |
1823 |
| Permanent link to this record |
| |
|
| |
| Author |
C. Alejandro Parraga; Olivier Penacchio; Maria Vanrell |
| Title |
Retinal Filtering Matches Natural Image Statistics at Low Luminance Levels |
Type |
Journal Article |
| Year |
2011 |
Publication |
Perception |
Abbreviated Journal |
PER |
| Volume |
40 |
Issue |
|
Pages |
96 |
| Keywords |
|
| Abstract |
The assumption that the retina’s main objective is to provide a minimum entropy representation to higher visual areas (ie efficient coding principle) allows to predict retinal filtering in space–time and colour (Atick, 1992 Network 3 213–251). This is achieved by considering the power spectra of natural images (which is proportional to 1/f2) and the suppression of retinal and image noise. However, most studies consider images within a limited range of lighting conditions (eg near noon) whereas the visual system’s spatial filtering depends on light intensity and the spatiochromatic properties of natural scenes depend of the time of the day. Here, we explore whether the dependence of visual spatial filtering on luminance match the changes in power spectrum of natural scenes at different times of the day. Using human cone-activation based naturalistic stimuli (from the Barcelona Calibrated Images Database), we show that for a range of luminance levels, the shape of the retinal CSF reflects the slope of the power spectrum at low spatial frequencies. Accordingly, the retina implements the filtering which best decorrelates the input signal at every luminance level. This result is in line with the body of work that places efficient coding as a guiding neural principle. |
| Address |
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| Corporate Author |
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Thesis |
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| Publisher |
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Place of Publication |
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Editor |
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| Language |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
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ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
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| Notes |
CIC |
Approved |
no |
| Call Number |
Admin @ si @ PPV2011 |
Serial |
1720 |
| Permanent link to this record |
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| |
| Author |
Simone Balocco; Carlo Gatta; Francesco Ciompi; Oriol Pujol; Xavier Carrillo; J. Mauri; Petia Radeva |
| Title |
Combining Growcut and Temporal Correlation for IVUS Lumen Segmentation |
Type |
Conference Article |
| Year |
2011 |
Publication |
5th Iberian Conference on Pattern Recognition and Image Analysis |
Abbreviated Journal |
|
| Volume |
6669 |
Issue |
|
Pages |
556-563 |
| Keywords |
|
| Abstract |
The assessment of arterial luminal area, performed by IVUS analysis, is a clinical index used to evaluate the degree of coronary artery disease. In this paper we propose a novel approach to automatically segment the vessel lumen, which combines model-based temporal information extracted from successive frames of the sequence, with spatial classification using the Growcut algorithm. The performance of the method is evaluated by an in vivo experiment on 300 IVUS frames. The automatic and manual segmentation performances in general vessel and stent frames are comparable. The average segmentation error in vessel, stent and bifurcation frames are 0.17±0.08 mm, 0.18±0.07 mm and 0.31±0.12 mm respectively. |
| Address |
Las Palmas de Gran Canaria. Spain |
| Corporate Author |
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Thesis |
|
| Publisher |
Springer Berlin Heidelberg |
Place of Publication |
Berlin |
Editor |
Jordi Vitria; Joao Miguel Raposo; Mario Hernandez |
| Language |
|
Summary Language |
|
Original Title |
|
| Series Editor |
|
Series Title |
|
Abbreviated Series Title |
LNCS |
| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
0302-9743 |
ISBN |
978-3-642-21256-7 |
Medium |
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| Area |
|
Expedition |
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Conference |
IbPRIA |
| Notes |
MILAB;HuPBA |
Approved |
no |
| Call Number |
Admin @ si @ BGC2011a |
Serial |
1741 |
| Permanent link to this record |
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| |
| Author |
Mario Rojas; David Masip; Jordi Vitria |
| Title |
Predicting Dominance Judgements Automatically: A Machine Learning Approach. |
Type |
Conference Article |
| Year |
2011 |
Publication |
IEEE International Workshop on Social Behavior Analysis |
Abbreviated Journal |
|
| Volume |
|
Issue |
|
Pages |
939-944 |
| Keywords |
|
| Abstract |
The amount of multimodal devices that surround us is growing everyday. In this context, human interaction and communication have become a focus of attention and a hot topic of research. A crucial element in human relations is the evaluation of individuals with respect to facial traits, what is called a first impression. Studies based on appearance have suggested that personality can be expressed by appearance and the observer may use such information to form judgments. In the context of rapid facial evaluation, certain personality traits seem to have a more pronounced effect on the relations and perceptions inside groups. The perception of dominance has been shown to be an active part of social roles at different stages of life, and even play a part in mate selection. The aim of this paper is to study to what extent this information is learnable from the point of view of computer science. Specifically we intend to determine if judgments of dominance can be learned by machine learning techniques. We implement two different descriptors in order to assess this. The first is the histogram of oriented gradients (HOG), and the second is a probabilistic appearance descriptor based on the frequencies of grouped binary tests. State of the art classification rules validate the performance of both descriptors, with respect to the prediction task. Experimental results show that machine learning techniques can predict judgments of dominance rather accurately (accuracies up to 90%) and that the HOG descriptor may characterize appropriately the information necessary for such task. |
| Address |
Santa Barbara, CA |
| Corporate Author |
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Thesis |
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| Publisher |
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Place of Publication |
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Editor |
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| Language |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
|
ISBN |
978-1-4244-9140-7 |
Medium |
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| Area |
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Expedition |
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Conference |
SBA |
| Notes |
OR;MV |
Approved |
no |
| Call Number |
Admin @ si @ RMV2011b |
Serial |
1760 |
| Permanent link to this record |
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|
| |
| Author |
Carles Fernandez; Pau Baiget; Xavier Roca; Jordi Gonzalez |
| Title |
Determining the Best Suited Semantic Events for Cognitive Surveillance |
Type |
Journal Article |
| Year |
2011 |
Publication |
Expert Systems with Applications |
Abbreviated Journal |
EXSY |
| Volume |
38 |
Issue |
4 |
Pages |
4068–4079 |
| Keywords |
Cognitive surveillance; Event modeling; Content-based video retrieval; Ontologies; Advanced user interfaces |
| Abstract |
State-of-the-art systems on cognitive surveillance identify and describe complex events in selected domains, thus providing end-users with tools to easily access the contents of massive video footage. Nevertheless, as the complexity of events increases in semantics and the types of indoor/outdoor scenarios diversify, it becomes difficult to assess which events describe better the scene, and how to model them at a pixel level to fulfill natural language requests. We present an ontology-based methodology that guides the identification, step-by-step modeling, and generalization of the most relevant events to a specific domain. Our approach considers three steps: (1) end-users provide textual evidence from surveilled video sequences; (2) transcriptions are analyzed top-down to build the knowledge bases for event description; and (3) the obtained models are used to generalize event detection to different image sequences from the surveillance domain. This framework produces user-oriented knowledge that improves on existing advanced interfaces for video indexing and retrieval, by determining the best suited events for video understanding according to end-users. We have conducted experiments with outdoor and indoor scenes showing thefts, chases, and vandalism, demonstrating the feasibility and generalization of this proposal. |
| Address |
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| Corporate Author |
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Thesis |
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| Publisher |
Elsevier |
Place of Publication |
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Editor |
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| Language |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
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ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
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| Notes |
ISE |
Approved |
no |
| Call Number |
Admin @ si @ FBR2011a |
Serial |
1722 |
| Permanent link to this record |
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| |
| Author |
Debora Gil; Aura Hernandez-Sabate; Mireia Brunat;Steven Jansen; Jordi Martinez-Vilalta |
| Title |
Structure-preserving smoothing of biomedical images |
Type |
Journal Article |
| Year |
2011 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
| Volume |
44 |
Issue |
9 |
Pages |
1842-1851 |
| Keywords |
Non-linear smoothing; Differential geometry; Anatomical structures; segmentation; Cardiac magnetic resonance; Computerized tomography |
| Abstract |
Smoothing of biomedical images should preserve gray-level transitions between adjacent tissues, while restoring contours consistent with anatomical structures. Anisotropic diffusion operators are based on image appearance discontinuities (either local or contextual) and might fail at weak inter-tissue transitions. Meanwhile, the output of block-wise and morphological operations is prone to present a block structure due to the shape and size of the considered pixel neighborhood. In this contribution, we use differential geometry concepts to define a diffusion operator that restricts to image consistent level-sets. In this manner, the final state is a non-uniform intensity image presenting homogeneous inter-tissue transitions along anatomical structures, while smoothing intra-structure texture. Experiments on different types of medical images (magnetic resonance, computerized tomography) illustrate its benefit on a further process (such as segmentation) of images. |
| Address |
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| Corporate Author |
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Thesis |
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| Publisher |
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Place of Publication |
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Editor |
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| Language |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
0031-3203 |
ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
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| Notes |
IAM; ADAS |
Approved |
no |
| Call Number |
IAM @ iam @ GHB2011 |
Serial |
1526 |
| Permanent link to this record |
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| |
| Author |
Marcel P. Lucassen; Theo Gevers; Arjan Gijsenij |
| Title |
Texture Affects Color Emotion |
Type |
Journal Article |
| Year |
2011 |
Publication |
Color Research & Applications |
Abbreviated Journal |
CRA |
| Volume |
36 |
Issue |
6 |
Pages |
426–436 |
| Keywords |
color;texture;color emotion;observer variability;ranking |
| Abstract |
Several studies have recorded color emotions in subjects viewing uniform color (UC) samples. We conduct an experiment to measure and model how these color emotions change when texture is added to the color samples. Using a computer monitor, our subjects arrange samples along four scales: warm–cool, masculine–feminine, hard–soft, and heavy–light. Three sample types of increasing visual complexity are used: UC, grayscale textures, and color textures (CTs). To assess the intraobserver variability, the experiment is repeated after 1 week. Our results show that texture fully determines the responses on the Hard-Soft scale, and plays a role of decreasing weight for the masculine–feminine, heavy–light, and warm–cool scales. Using some 25,000 observer responses, we derive color emotion functions that predict the group-averaged scale responses from the samples' color and texture parameters. For UC samples, the accuracy of our functions is significantly higher (average R2 = 0.88) than that of previously reported functions applied to our data. The functions derived for CT samples have an accuracy of R2 = 0.80. We conclude that when textured samples are used in color emotion studies, the psychological responses may be strongly affected by texture. © 2010 Wiley Periodicals, Inc. Col Res Appl, 2010 |
| Address |
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| Corporate Author |
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Thesis |
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| Publisher |
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Place of Publication |
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Editor |
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| Language |
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Summary Language |
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Original Title |
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Series Title |
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Abbreviated Series Title |
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Series Issue |
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Edition |
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ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
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| Notes |
ALTRES;ISE |
Approved |
no |
| Call Number |
Admin @ si @ LGG2011 |
Serial |
1844 |
| Permanent link to this record |
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| |
| Author |
Lluis Pere de las Heras; Joan Mas; Gemma Sanchez; Ernest Valveny |
| Title |
Wall Patch-Based Segmentation in Architectural Floorplans |
Type |
Conference Article |
| Year |
2011 |
Publication |
11th International Conference on Document Analysis and Recognition |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
1270-1274 |
| Keywords |
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| Abstract |
Segmentation of architectural floor plans is a challenging task, mainly because of the large variability in the notation between different plans. In general, traditional techniques, usually based on analyzing and grouping structural primitives obtained by vectorization, are only able to handle a reduced range of similar notations. In this paper we propose an alternative patch-based segmentation approach working at pixel level, without need of vectorization. The image is divided into a set of patches and a set of features is extracted for every patch. Then, each patch is assigned to a visual word of a previously learned vocabulary and given a probability of belonging to each class of objects. Finally, a post-process assigns the final label for every pixel. This approach has been applied to the detection of walls on two datasets of architectural floor plans with different notations, achieving high accuracy rates. |
| Address |
Beiging, China |
| Corporate Author |
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Thesis |
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| Publisher |
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Place of Publication |
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Editor |
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| Language |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
1520-5363 |
ISBN |
978-0-7695-4520-2 |
Medium |
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| Area |
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Expedition |
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Conference |
ICDAR |
| Notes |
DAG |
Approved |
no |
| Call Number |
Admin @ si @ HMS2011a |
Serial |
1792 |
| Permanent link to this record |
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| |
| Author |
Joan M. Nuñez |
| Title |
Computer vision techniques for characterization of finger joints in X-ray image |
Type |
Report |
| Year |
2011 |
Publication |
CVC Technical Report |
Abbreviated Journal |
|
| Volume |
165 |
Issue |
|
Pages |
|
| Keywords |
Rheumatoid arthritis, X-ray, Sharp Van der Heijde, joint characterization, sclerosis detection, bone detection, edge, ridge |
| Abstract |
Rheumatoid arthritis (RA) is an autoimmune inflammatory type of arthritis which mainly affects hands on its first stages. Though it is a chronic disease and there is no cure for it, treatments require an accurate assessment of illness evolution. Such assessment is based on evaluation of hand X-ray images by using one of the several available semi-quantitative methods. This task requires highly trained medical personnel. That is why the automation of the assessment would allow professionals to save time and effort. Two stages are involved in this task. Firstly, the joint detection, afterwards, the joint characterization. Unlike the little existing previous work, this contribution clearly separates those two stages and sets the foundations of a modular assessment system focusing on the characterization stage. A hand joint dataset is created and an accurate data analysis is achieved in order to identify relevant features. Since the sclerosis and the lower bone were decided to be the most important features, different computer vision techniques were used in order to develop a detector system for both of them. Joint space width measures are provided and their correlation with Sharp-Van der Heijde is verified |
| Address |
Bellaterra (Barcelona) |
| Corporate Author |
Computer Vision Center |
Thesis |
Master's thesis |
| Publisher |
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Place of Publication |
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Editor |
Dr. Fernando Vilariño and Dra. Debora Gil |
| Language |
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Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
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| Notes |
MV;IAM; |
Approved |
no |
| Call Number |
IAM @ iam @ Nuñ2011 |
Serial |
1795 |
| Permanent link to this record |
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| |
| Author |
Bhaskar Chakraborty; Michael Holte; Thomas B. Moeslund; Jordi Gonzalez; Xavier Roca |
| Title |
A Selective Spatio-Temporal Interest Point Detector for Human Action Recognition in Complex Scenes |
Type |
Conference Article |
| Year |
2011 |
Publication |
13th IEEE International Conference on Computer Vision |
Abbreviated Journal |
|
| Volume |
|
Issue |
|
Pages |
1776-1783 |
| Keywords |
|
| Abstract |
Recent progress in the field of human action recognition points towards the use of Spatio-Temporal Interest Points (STIPs) for local descriptor-based recognition strategies. In this paper we present a new approach for STIP detection by applying surround suppression combined with local and temporal constraints. Our method is significantly different from existing STIP detectors and improves the performance by detecting more repeatable, stable and distinctive STIPs for human actors, while suppressing unwanted background STIPs. For action representation we use a bag-of-visual words (BoV) model of local N-jet features to build a vocabulary of visual-words. To this end, we introduce a novel vocabulary building strategy by combining spatial pyramid and vocabulary compression techniques, resulting in improved performance and efficiency. Action class specific Support Vector Machine (SVM) classifiers are trained for categorization of human actions. A comprehensive set of experiments on existing benchmark datasets, and more challenging datasets of complex scenes, validate our approach and show state-of-the-art performance. |
| Address |
Barcelona |
| Corporate Author |
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Thesis |
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Place of Publication |
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Editor |
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| Language |
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Summary Language |
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Original Title |
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Series Title |
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Abbreviated Series Title |
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Series Issue |
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Edition |
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| ISSN |
1550-5499 |
ISBN |
978-1-4577-1101-5 |
Medium |
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| Area |
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Expedition |
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Conference |
ICCV |
| Notes |
ISE |
Approved |
no |
| Call Number |
Admin @ si @ CHM2011 |
Serial |
1811 |
| Permanent link to this record |
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| Author |
Fahad Shahbaz Khan |
| Title |
Coloring bag-of-words based image representations |
Type |
Book Whole |
| Year |
2011 |
Publication |
PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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| Abstract |
Put succinctly, the bag-of-words based image representation is the most successful approach for object and scene recognition. Within the bag-of-words framework the optimal fusion of multiple cues, such as shape, texture and color, still remains an active research domain. There exist two main approaches to combine color and shape information within the bag-of-words framework. The first approach called, early fusion, fuses color and shape at the feature level as a result of which a joint colorshape vocabulary is produced. The second approach, called late fusion, concatenates histogram representation of both color and shape, obtained independently. In the first part of this thesis, we analyze the theoretical implications of both early and late feature fusion. We demonstrate that both these approaches are suboptimal for a subset of object categories. Consequently, we propose a novel method for recognizing object categories when using multiple cues by separately processing the shape and color cues and combining them by modulating the shape features by category specific color attention. Color is used to compute bottom-up and top-down attention maps. Subsequently, the color attention maps are used to modulate the weights of the shape features. Shape features are given more weight in regions with higher attention and vice versa. The approach is tested on several benchmark object recognition data sets and the results clearly demonstrate the effectiveness of our proposed method. In the second part of the thesis, we investigate the problem of obtaining compact spatial pyramid representations for object and scene recognition. Spatial pyramids have been successfully applied to incorporate spatial information into bag-of-words based image representation. However, a major drawback of spatial pyramids is that it leads to high dimensional image representations. We present a novel framework for obtaining compact pyramid representation. The approach reduces the size of a high dimensional pyramid representation upto an order of magnitude without any significant reduction in accuracy. Moreover, we also investigate the optimal combination of multiple features such as color and shape within the context of our compact pyramid representation. Finally, we describe a novel technique to build discriminative visual words from multiple cues learned independently from training images. To this end, we use an information theoretic vocabulary compression technique to find discriminative combinations of visual cues and the resulting visual vocabulary is compact, has the cue binding property, and supports individual weighting of cues in the final image representation. The approach is tested on standard object recognition data sets. The results obtained clearly demonstrate the effectiveness of our approach. |
| Address |
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| Corporate Author |
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Thesis |
Ph.D. thesis |
| Publisher |
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Place of Publication |
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Editor |
Joost Van de Weijer;Maria Vanrell |
| Language |
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Summary Language |
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Original Title |
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Expedition |
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Conference |
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| Notes |
CIC |
Approved |
no |
| Call Number |
Admin @ si @ Kha2011 |
Serial |
1838 |
| Permanent link to this record |
