| Records |
| Author |
Carlo Gatta; Eloi Puertas; Oriol Pujol |
| Title |
Multi-Scale Stacked Sequential Learning |
Type |
Journal Article |
| Year |
2011 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
| Volume |
44 |
Issue |
10-11 |
Pages |
2414-2416 |
| Keywords |
Stacked sequential learning; Multiscale; Multiresolution; Contextual classification |
| Abstract |
One of the most widely used assumptions in supervised learning is that data is independent and identically distributed. This assumption does not hold true in many real cases. Sequential learning is the discipline of machine learning that deals with dependent data such that neighboring examples exhibit some kind of relationship. In the literature, there are different approaches that try to capture and exploit this correlation, by means of different methodologies. In this paper we focus on meta-learning strategies and, in particular, the stacked sequential learning approach. The main contribution of this work is two-fold: first, we generalize the stacked sequential learning. This generalization reflects the key role of neighboring interactions modeling. Second, we propose an effective and efficient way of capturing and exploiting sequential correlations that takes into account long-range interactions by means of a multi-scale pyramidal decomposition of the predicted labels. Additionally, this new method subsumes the standard stacked sequential learning approach. We tested the proposed method on two different classification tasks: text lines classification in a FAQ data set and image classification. Results on these tasks clearly show that our approach outperforms the standard stacked sequential learning. Moreover, we show that the proposed method allows to control the trade-off between the detail and the desired range of the interactions. |
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Elsevier |
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LNCS |
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| Notes |
MILAB;HuPBA |
Approved |
no |
| Call Number |
Admin @ si @ GPP2011 |
Serial  |
1802 |
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| Author |
E. Serradell; Adriana Romero; R. Leta; Carlo Gatta; Francesc Moreno-Noguer |
| Title |
Simultaneous Correspondence and Non-Rigid 3D Reconstruction of the Coronary Tree from Single X-Ray Images |
Type |
Conference Article |
| Year |
2011 |
Publication |
13th IEEE International Conference on Computer Vision |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
850-857 |
| Keywords |
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| Abstract |
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| Address |
Barcelona |
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Expedition |
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Conference |
ICCV |
| Notes |
MILAB |
Approved |
no |
| Call Number |
Admin @ si @ SRL2011 |
Serial |
1803 |
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| Author |
Bogdan Raducanu; Fadi Dornaika |
| Title |
A Discriminative Non-Linear Manifold Learning Technique for Face Recognition |
Type |
Book Chapter |
| Year |
2011 |
Publication |
Informatics Engineering and Information Science |
Abbreviated Journal |
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| Volume |
254 |
Issue |
6 |
Pages |
339-353 |
| Keywords |
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| Abstract |
In this paper we propose a novel non-linear discriminative analysis technique for manifold learning. The proposed approach is a discriminant version of Laplacian Eigenmaps which takes into account the class label information in order to guide the procedure of non-linear dimensionality reduction. By following the large margin concept, the graph Laplacian is split in two components: within-class graph and between-class graph to better characterize the discriminant property of the data.
Our approach has been tested on several challenging face databases and it has been conveniently compared with other linear and non-linear techniques. The experimental results confirm that our method outperforms, in general, the existing ones. Although we have concentrated in this paper on the face recognition problem, the proposed approach could also be applied to other category of objects characterized by large variance in their appearance. |
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| Publisher |
Springer Berlin Heidelberg |
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Edition |
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| ISSN |
1865-0929 |
ISBN |
978-3-642-25482-6 |
Medium |
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Expedition |
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Conference |
ICIEIS |
| Notes |
OR;MV |
Approved |
no |
| Call Number |
Admin @ si @ RaD2011 |
Serial |
1804 |
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| Author |
Bhaskar Chakraborty; Michael Holte; Thomas B. Moeslund; Jordi Gonzalez |
| Title |
Selective Spatio-Temporal Interest Points |
Type |
Journal Article |
| Year |
2012 |
Publication |
Computer Vision and Image Understanding |
Abbreviated Journal |
CVIU |
| Volume |
116 |
Issue |
3 |
Pages |
396-410 |
| Keywords |
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| Abstract |
Recent progress in the field of human action recognition points towards the use of Spatio-TemporalInterestPoints (STIPs) for local descriptor-based recognition strategies. In this paper, we present a novel approach for robust and selective STIP detection, by applying surround suppression combined with local and temporal constraints. This new method is significantly different from existing STIP detection techniques 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-video 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 popular benchmark datasets (KTH and Weizmann), more challenging datasets of complex scenes with background clutter and camera motion (CVC and CMU), movie and YouTube video clips (Hollywood 2 and YouTube), and complex scenes with multiple actors (MSR I and Multi-KTH), validates our approach and show state-of-the-art performance. Due to the unavailability of ground truth action annotation data for the Multi-KTH dataset, we introduce an actor specific spatio-temporal clustering of STIPs to address the problem of automatic action annotation of multiple simultaneous actors. Additionally, we perform cross-data action recognition by training on source datasets (KTH and Weizmann) and testing on completely different and more challenging target datasets (CVC, CMU, MSR I and Multi-KTH). This documents the robustness of our proposed approach in the realistic scenario, using separate training and test datasets, which in general has been a shortcoming in the performance evaluation of human action recognition techniques. |
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Thesis |
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| Publisher |
Elsevier |
Place of Publication |
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Edition |
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| ISSN |
1077-3142 |
ISBN |
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Medium |
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Expedition |
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Conference |
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| Notes |
ISE |
Approved |
no |
| Call Number |
Admin @ si @ CHM2012 |
Serial |
1806 |
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| Author |
Noha Elfiky; Fahad Shahbaz Khan; Joost Van de Weijer; Jordi Gonzalez |
| Title |
Discriminative Compact Pyramids for Object and Scene Recognition |
Type |
Journal Article |
| Year |
2012 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
| Volume |
45 |
Issue |
4 |
Pages |
1627-1636 |
| Keywords |
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| Abstract |
Spatial pyramids have been successfully applied to incorporating spatial information into bag-of-words based image representation. However, a major drawback is that it leads to high dimensional image representations. In this paper, we present a novel framework for obtaining compact pyramid representation. First, we investigate the usage of the divisive information theoretic feature clustering (DITC) algorithm in creating a compact pyramid representation. In many cases this method allows us to reduce the size of a high dimensional pyramid representation up to an order of magnitude with little or no loss in accuracy. Furthermore, comparison to clustering based on agglomerative information bottleneck (AIB) shows that our method obtains superior results at significantly lower computational costs. Moreover, we investigate the optimal combination of multiple features in the context of our compact pyramid representation. Finally, experiments show that the method can obtain state-of-the-art results on several challenging data sets. |
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Place of Publication |
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Edition |
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| ISSN |
0031-3203 |
ISBN |
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Medium |
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Expedition |
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Conference |
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| Notes |
ISE; CAT;CIC |
Approved |
no |
| Call Number |
Admin @ si @ EKW2012 |
Serial |
1807 |
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| Author |
Ivan Huerta; Ariel Amato; Xavier Roca; Jordi Gonzalez |
| Title |
Exploiting Multiple Cues in Motion Segmentation Based on Background Subtraction |
Type |
Journal Article |
| Year |
2013 |
Publication |
Neurocomputing |
Abbreviated Journal |
NEUCOM |
| Volume |
100 |
Issue |
|
Pages |
183–196 |
| Keywords |
Motion segmentation; Shadow suppression; Colour segmentation; Edge segmentation; Ghost detection; Background subtraction |
| Abstract |
This paper presents a novel algorithm for mobile-object segmentation from static background scenes, which is both robust and accurate under most of the common problems found in motionsegmentation. In our first contribution, a case analysis of motionsegmentation errors is presented taking into account the inaccuracies associated with different cues, namely colour, edge and intensity. Our second contribution is an hybrid architecture which copes with the main issues observed in the case analysis by fusing the knowledge from the aforementioned three cues and a temporal difference algorithm. On one hand, we enhance the colour and edge models to solve not only global and local illumination changes (i.e. shadows and highlights) but also the camouflage in intensity. In addition, local information is also exploited to solve the camouflage in chroma. On the other hand, the intensity cue is applied when colour and edge cues are not available because their values are beyond the dynamic range. Additionally, temporal difference scheme is included to segment motion where those three cues cannot be reliably computed, for example in those background regions not visible during the training period. Lastly, our approach is extended for handling ghost detection. The proposed method obtains very accurate and robust motionsegmentation results in multiple indoor and outdoor scenarios, while outperforming the most-referred state-of-art approaches. |
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| Publisher |
Elsevier |
Place of Publication |
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| Notes |
ISE |
Approved |
no |
| Call Number |
Admin @ si @ HAR2013 |
Serial |
1808 |
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| Author |
Bhaskar Chakraborty; Andrew Bagdanov; Jordi Gonzalez; Xavier Roca |
| Title |
Human Action Recognition Using an Ensemble of Body-Part Detectors |
Type |
Journal Article |
| Year |
2013 |
Publication |
Expert Systems |
Abbreviated Journal |
EXSY |
| Volume |
30 |
Issue |
2 |
Pages |
101-114 |
| Keywords |
Human action recognition;body-part detection;hidden Markov model |
| Abstract |
This paper describes an approach to human action recognition based on a probabilistic optimization model of body parts using hidden Markov model (HMM). Our method is able to distinguish between similar actions by only considering the body parts having major contribution to the actions, for example, legs for walking, jogging and running; arms for boxing, waving and clapping. We apply HMMs to model the stochastic movement of the body parts for action recognition. The HMM construction uses an ensemble of body-part detectors, followed by grouping of part detections, to perform human identification. Three example-based body-part detectors are trained to detect three components of the human body: the head, legs and arms. These detectors cope with viewpoint changes and self-occlusions through the use of ten sub-classifiers that detect body parts over a specific range of viewpoints. Each sub-classifier is a support vector machine trained on features selected for the discriminative power for each particular part/viewpoint combination. Grouping of these detections is performed using a simple geometric constraint model that yields a viewpoint-invariant human detector. We test our approach on three publicly available action datasets: the KTH dataset, Weizmann dataset and HumanEva dataset. Our results illustrate that with a simple and compact representation we can achieve robust recognition of human actions comparable to the most complex, state-of-the-art methods. |
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| Notes |
ISE |
Approved |
no |
| Call Number |
Admin @ si @ CBG2013 |
Serial |
1809 |
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| Author |
Nataliya Shapovalova; Carles Fernandez; Xavier Roca; Jordi Gonzalez |
| Title |
Semantics of Human Behavior in Image Sequences |
Type |
Book Chapter |
| Year |
2011 |
Publication |
Computer Analysis of Human Behavior |
Abbreviated Journal |
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| Volume |
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Issue |
7 |
Pages |
151-182 |
| Keywords |
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| Abstract |
Human behavior is contextualized and understanding the scene of an action is crucial for giving proper semantics to behavior. In this chapter we present a novel approach for scene understanding. The emphasis of this work is on the particular case of Human Event Understanding. We introduce a new taxonomy to organize the different semantic levels of the Human Event Understanding framework proposed. Such a framework particularly contributes to the scene understanding domain by (i) extracting behavioral patterns from the integrative analysis of spatial, temporal, and contextual evidence and (ii) integrative analysis of bottom-up and top-down approaches in Human Event Understanding. We will explore how the information about interactions between humans and their environment influences the performance of activity recognition, and how this can be extrapolated to the temporal domain in order to extract higher inferences from human events observed in sequences of images. |
| Address |
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| Publisher |
Springer London |
Place of Publication |
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Editor |
Albert Ali Salah; |
| Language |
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Summary Language |
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Original Title |
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ISBN |
978-0-85729-993-2 |
Medium |
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Conference |
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| Notes |
ISE |
Approved |
no |
| Call Number |
Admin @ si @ SFR2011 |
Serial |
1810 |
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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 |
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| Volume |
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Issue |
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Pages |
1776-1783 |
| Keywords |
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| 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 |
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Edition |
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| ISSN |
1550-5499 |
ISBN |
978-1-4577-1101-5 |
Medium |
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Conference |
ICCV |
| Notes |
ISE |
Approved |
no |
| Call Number |
Admin @ si @ CHM2011 |
Serial |
1811 |
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| Author |
Wenjuan Gong; Jürgen Brauer; Michael Arens; Jordi Gonzalez |
| Title |
Modeling vs. Learning Approaches for Monocular 3D Human Pose Estimation |
Type |
Conference Article |
| Year |
2011 |
Publication |
1st IEEE International Workshop on Performance Evaluation on Recognition of Human Actions and Pose Estimation Methods |
Abbreviated Journal |
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Issue |
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Pages |
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| Address |
London, United Kingdom |
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Conference |
PERHAPS |
| Notes |
ISE |
Approved |
no |
| Call Number |
Admin @ si @ GBA2011 |
Serial |
1812 |
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| Author |
Jordi Gonzalez; Josep M. Gonfaus; Carles Fernandez; Xavier Roca |
| Title |
Exploiting Natural-Language Interaction in Video Surveillance Systems |
Type |
Conference Article |
| Year |
2011 |
Publication |
V&L Net Workshop on Vision and Language |
Abbreviated Journal |
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Pages |
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| Address |
Brighton, UK |
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Conference |
VL |
| Notes |
ISE |
Approved |
no |
| Call Number |
Admin @ si @ GGF2011 |
Serial |
1813 |
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| Author |
Murad Al Haj; Carles Fernandez; Zhanwu Xiong; Ivan Huerta; Jordi Gonzalez; Xavier Roca |
| Title |
Beyond the Static Camera: Issues and Trends in Active Vision |
Type |
Book Chapter |
| Year |
2011 |
Publication |
Visual Analysis of Humans: Looking at People |
Abbreviated Journal |
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| Volume |
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Issue |
2 |
Pages |
11-30 |
| Keywords |
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| Abstract |
Maximizing both the area coverage and the resolution per target is highly desirable in many applications of computer vision. However, with a limited number of cameras viewing a scene, the two objectives are contradictory. This chapter is dedicated to active vision systems, trying to achieve a trade-off between these two aims and examining the use of high-level reasoning in such scenarios. The chapter starts by introducing different approaches to active cameras configurations. Later, a single active camera system to track a moving object is developed, offering the reader first-hand understanding of the issues involved. Another section discusses practical considerations in building an active vision platform, taking as an example a multi-camera system developed for a European project. The last section of the chapter reflects upon the future trends of using semantic factors to drive smartly coordinated active systems. |
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Springer London |
Place of Publication |
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Editor |
Th.B. Moeslund; A. Hilton; V. Krüger; L. Sigal |
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ISBN |
978-0-85729-996-3 |
Medium |
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| Notes |
ISE |
Approved |
no |
| Call Number |
Admin @ si @ AFX2011 |
Serial |
1814 |
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| Author |
Kaida Xiao; Chenyang Fu; Dimosthenis Karatzas; Sophie Wuerger |
| Title |
Visual Gamma Correction for LCD Displays |
Type |
Journal Article |
| Year |
2011 |
Publication |
Displays |
Abbreviated Journal |
DIS |
| Volume |
32 |
Issue |
1 |
Pages |
17-23 |
| Keywords |
Display calibration; Psychophysics ; Perceptual; Visual gamma correction; Luminance matching; Observer-based calibration |
| Abstract |
An improved method for visual gamma correction is developed for LCD displays to increase the accuracy of digital colour reproduction. Rather than utilising a photometric measurement device, we use observ- ers’ visual luminance judgements for gamma correction. Eight half tone patterns were designed to gen- erate relative luminances from 1/9 to 8/9 for each colour channel. A psychophysical experiment was conducted on an LCD display to find the digital signals corresponding to each relative luminance by visually matching the half-tone background to a uniform colour patch. Both inter- and intra-observer vari- ability for the eight luminance matches in each channel were assessed and the luminance matches proved to be consistent across observers (DE00 < 3.5) and repeatable (DE00 < 2.2). Based on the individual observer judgements, the display opto-electronic transfer function (OETF) was estimated by using either a 3rd order polynomial regression or linear interpolation for each colour channel. The performance of the proposed method is evaluated by predicting the CIE tristimulus values of a set of coloured patches (using the observer-based OETFs) and comparing them to the expected CIE tristimulus values (using the OETF obtained from spectro-radiometric luminance measurements). The resulting colour differences range from 2 to 4.6 DE00. We conclude that this observer-based method of visual gamma correction is useful to estimate the OETF for LCD displays. Its major advantage is that no particular functional relationship between digital inputs and luminance outputs has to be assumed. |
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| Publisher |
Elsevier |
Place of Publication |
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| Notes |
DAG |
Approved |
no |
| Call Number |
Admin @ si @ XFK2011 |
Serial |
1815 |
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| Author |
Kaida Xiao; Sophie Wuerger; Chenyang Fu; Dimosthenis Karatzas |
| Title |
Unique Hue Data for Colour Appearance Models. Part i: Loci of Unique Hues and Hue Uniformity |
Type |
Journal Article |
| Year |
2011 |
Publication |
Color Research & Application |
Abbreviated Journal |
CRA |
| Volume |
36 |
Issue |
5 |
Pages |
316-323 |
| Keywords |
unique hues; colour appearance models; CIECAM02; hue uniformity |
| Abstract |
Psychophysical experiments were conducted to assess unique hues on a CRT display for a large sample of colour-normal observers (n 1⁄4 185). These data were then used to evaluate the most commonly used colour appear- ance model, CIECAM02, by transforming the CIEXYZ tris- timulus values of the unique hues to the CIECAM02 colour appearance attributes, lightness, chroma and hue angle. We report two findings: (1) the hue angles derived from our unique hue data are inconsistent with the commonly used Natural Color System hues that are incorporated in the CIECAM02 model. We argue that our predicted unique hue angles (derived from our large dataset) provide a more reliable standard for colour management applications when the precise specification of these salient colours is im- portant. (2) We test hue uniformity for CIECAM02 in all four unique hues and show significant disagreements for all hues, except for unique red which seems to be invariant under lightness changes. Our dataset is useful to improve the CIECAM02 model as it provides reliable data for benchmarking. |
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| Publisher |
Wiley Periodicals Inc |
Place of Publication |
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DAG |
Approved |
no |
| Call Number |
Admin @ si @ XWF2011 |
Serial |
1816 |
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| Author |
Albert Gordo; Florent Perronnin |
| Title |
Asymmetric Distances for Binary Embeddings |
Type |
Conference Article |
| Year |
2011 |
Publication |
IEEE Conference on Computer Vision and Pattern Recognition |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
729 - 736 |
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| Abstract |
In large-scale query-by-example retrieval, embedding image signatures in a binary space offers two benefits: data compression and search efficiency. While most embedding algorithms binarize both query and database signatures, it has been noted that this is not strictly a requirement. Indeed, asymmetric schemes which binarize the database signatures but not the query still enjoy the same two benefits but may provide superior accuracy. In this work, we propose two general asymmetric distances which are applicable to a wide variety of embedding techniques including Locality Sensitive Hashing (LSH), Locality Sensitive Binary Codes (LSBC), Spectral Hashing (SH) and Semi-Supervised Hashing (SSH). We experiment on four public benchmarks containing up to 1M images and show that the proposed asymmetric distances consistently lead to large improvements over the symmetric Hamming distance for all binary embedding techniques. We also propose a novel simple binary embedding technique – PCA Embedding (PCAE) – which is shown to yield competitive results with respect to more complex algorithms such as SH and SSH. |
| Address |
Providence, RI |
| 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 |
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ISBN |
978-1-4577-0394-2 |
Medium |
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| Area |
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Expedition |
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Conference |
CVPR |
| Notes |
DAG |
Approved |
no |
| Call Number |
Admin @ si @ GoP2011; IAM @ iam @ GoP2011 |
Serial |
1817 |
| Permanent link to this record |
