| Records |
| Author |
Naila Murray; Luca Marchesotti; Florent Perronnin |
| Title |
Learning to Rank Images using Semantic and Aesthetic Labels |
Type |
Conference Article |
| Year |
2012 |
Publication |
23rd British Machine Vision Conference |
Abbreviated Journal |
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Volume  |
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Issue |
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Pages |
110.1-110.10 |
| Keywords |
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| Abstract |
Most works on image retrieval from text queries have addressed the problem of retrieving semantically relevant images. However, the ability to assess the aesthetic quality of an image is an increasingly important differentiating factor for search engines. In this work, given a semantic query, we are interested in retrieving images which are semantically relevant and score highly in terms of aesthetics/visual quality. We use large-margin classifiers and rankers to learn statistical models capable of ordering images based on the aesthetic and semantic information. In particular, we compare two families of approaches: while the first one attempts to learn a single ranker which takes into account both semantic and aesthetic information, the second one learns separate semantic and aesthetic models. We carry out a quantitative and qualitative evaluation on a recently-published large-scale dataset and we show that the second family of techniques significantly outperforms the first one. |
| Address |
Guildford, London |
| 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 |
1-901725-46-4 |
Medium |
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| Area |
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Expedition |
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Conference |
BMVC |
| Notes |
CIC |
Approved |
no |
| Call Number |
Admin @ si @ MMP2012b |
Serial |
2027 |
| Permanent link to this record |
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| Author |
Murad Al Haj; Jordi Gonzalez; Larry S. Davis |
| Title |
On Partial Least Squares in Head Pose Estimation: How to simultaneously deal with misalignment |
Type |
Conference Article |
| Year |
2012 |
Publication |
25th 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 |
2602-2609 |
| Keywords |
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| Abstract |
Head pose estimation is a critical problem in many computer vision applications. These include human computer interaction, video surveillance, face and expression recognition. In most prior work on heads pose estimation, the positions of the faces on which the pose is to be estimated are specified manually. Therefore, the results are reported without studying the effect of misalignment. We propose a method based on partial least squares (PLS) regression to estimate pose and solve the alignment problem simultaneously. The contributions of this paper are two-fold: 1) we show that the kernel version of PLS (kPLS) achieves better than state-of-the-art results on the estimation problem and 2) we develop a technique to reduce misalignment based on the learned PLS factors. |
| Address |
Providence, Rhode Island |
| Corporate Author |
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Thesis |
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| Publisher |
IEEE Xplore |
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 |
1063-6919 |
ISBN |
978-1-4673-1226-4 |
Medium |
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| Area |
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Expedition |
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Conference |
CVPR |
| Notes |
ISE |
Approved |
no |
| Call Number |
Admin @ si @ HGD2012 |
Serial |
2029 |
| Permanent link to this record |
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| Author |
German Ros; Jesus Martinez del Rincon; Gines Garcia-Mateos |
| Title |
Articulated Particle Filter for Hand Tracking |
Type |
Conference Article |
| Year |
2012 |
Publication |
21st International Conference on Pattern Recognition |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
3581 - 3585 |
| Keywords |
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| Abstract |
This paper proposes a new version of Particle Filter, called Articulated Particle Filter – ArPF -, which has been specifically designed for an efficient sampling of hierarchical spaces, generated by articulated objects. Our approach decomposes the articulated motion into layers for efficiency purposes, making use of a careful modeling of the diffusion noise along with its propagation through the articulations. This produces an increase of accuracy and prevent for divergences. The algorithm is tested on hand tracking due to its complex hierarchical articulated nature. With this purpose, a new dataset generation tool for quantitative evaluation is also presented in this paper. |
| Address |
Tsukuba Science City, Japan |
| 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 |
1051-4651 |
ISBN |
978-1-4673-2216-4 |
Medium |
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| Area |
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Expedition |
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Conference |
ICPR |
| Notes |
ADAS |
Approved |
no |
| Call Number |
Admin @ si @ RMG2012 |
Serial |
2031 |
| Permanent link to this record |
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| Author |
Jose Carlos Rubio; Joan Serrat; Antonio Lopez |
| Title |
Unsupervised co-segmentation through region matching |
Type |
Conference Article |
| Year |
2012 |
Publication |
25th 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 |
749-756 |
| Keywords |
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| Abstract |
Co-segmentation is defined as jointly partitioning multiple images depicting the same or similar object, into foreground and background. Our method consists of a multiple-scale multiple-image generative model, which jointly estimates the foreground and background appearance distributions from several images, in a non-supervised manner. In contrast to other co-segmentation methods, our approach does not require the images to have similar foregrounds and different backgrounds to function properly. Region matching is applied to exploit inter-image information by establishing correspondences between the common objects that appear in the scene. Moreover, computing many-to-many associations of regions allow further applications, like recognition of object parts across images. We report results on iCoseg, a challenging dataset that presents extreme variability in camera viewpoint, illumination and object deformations and poses. We also show that our method is robust against large intra-class variability in the MSRC database. |
| Address |
Providence, Rhode Island |
| Corporate Author |
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Thesis |
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| Publisher |
IEEE Xplore |
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 |
1063-6919 |
ISBN |
978-1-4673-1226-4 |
Medium |
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| Area |
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Expedition |
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Conference |
CVPR |
| Notes |
ADAS |
Approved |
no |
| Call Number |
Admin @ si @ RSL2012b; ADAS @ adas @ |
Serial |
2033 |
| Permanent link to this record |
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| |
| Author |
Jose Carlos Rubio; Joan Serrat; Antonio Lopez |
| Title |
Multiple target tracking and identity linking under split, merge and occlusion of targets and observations |
Type |
Conference Article |
| Year |
2012 |
Publication |
1st International Conference on Pattern Recognition Applications and Methods |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
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| Keywords |
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| Abstract |
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| Address |
Algarve, Portugal |
| 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 |
ICPRAM |
| Notes |
ADAS |
Approved |
no |
| Call Number |
Admin @ si @ RSL2012c; ADAS @ adas |
Serial |
2034 |
| Permanent link to this record |
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| Author |
Cesar Isaza; Joaquin Salas; Bogdan Raducanu |
| Title |
Synthetic ground truth dataset to detect shadow cast by static objects in outdoor |
Type |
Conference Article |
| Year |
2012 |
Publication |
1st International Workshop on Visual Interfaces for Ground Truth Collection in Computer Vision Applications |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
art. 11 |
| Keywords |
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| Abstract |
In this paper, we propose a precise synthetic ground truth dataset to study the problem of detection of the shadows cast by static objects in outdoor environments during extended periods of time (days). For our dataset, we have created a virtual scenario using a rendering software. To increase the realism of the simulated environment, we have defined the scenario in a precise geographical location. In our dataset the sun is by far the main illumination source. The sun position during the simulation time takes into consideration factors related to the geographical location, such as the latitude, longitude, elevation above sea level, and precise image capturing day and time. In our simulation the camera remains fixed. The dataset consists of seven days of simulation, from 10:00am to 5:00pm. Images are captured every 10 seconds. The shadows' ground truth is automatically computed by the rendering software. |
| Address |
Capri, Italy |
| 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 |
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ISBN |
978-1-4503-1405-3 |
Medium |
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| Area |
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Expedition |
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Conference |
VIGTA |
| Notes |
OR;MV |
Approved |
no |
| Call Number |
Admin @ si @ ISR2012a |
Serial |
2037 |
| Permanent link to this record |
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| Author |
Laura Igual; Joan Carles Soliva; Antonio Hernandez; Sergio Escalera; Oscar Vilarroya; Petia Radeva |
| Title |
Supervised Brain Segmentation and Classification in Diagnostic of Attention-Deficit/Hyperactivity Disorder |
Type |
Conference Article |
| Year |
2012 |
Publication |
High Performance Computing and Simulation, International Conference on |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
182-187 |
| Keywords |
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| Abstract |
This paper presents an automatic method for external and internal segmentation of the caudate nucleus in Magnetic Resonance Images (MRI) based on statistical and structural machine learning approaches. This method is applied in Attention-Deficit/Hyperactivity Disorder (ADHD) diagnosis. The external segmentation method adapts the Graph Cut energy-minimization model to make it suitable for segmenting small, low-contrast structures, such as the caudate nucleus. In particular, new energy function data and boundary potentials are defined and a supervised energy term based on contextual brain structures is added. Furthermore, the internal segmentation method learns a classifier based on shape features of the Region of Interest (ROI) in MRI slices. The results show accurate external and internal caudate segmentation in a real data set and similar performance of ADHD diagnostic test to manual annotation. |
| Address |
Madrid |
| Corporate Author |
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Thesis |
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| Publisher |
IEEE Xplore |
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 |
978-1-4673-2359-8 |
Medium |
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| Area |
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Expedition |
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Conference |
HPCS |
| Notes |
MILAB;HuPBA |
Approved |
no |
| Call Number |
Admin @ si @ ISH2012a |
Serial |
2038 |
| Permanent link to this record |
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| |
| Author |
Mohammad Ali Bagheri; Qigang Gao; Sergio Escalera |
| Title |
Three-Dimensional Design of Error Correcting Output Codes |
Type |
Conference Article |
| Year |
2012 |
Publication |
8th International Conference on Machine Learning and Data Mining |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
29- |
| Keywords |
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| Abstract |
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| Address |
Berlin, Germany |
| 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 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 |
MLDM |
| Notes |
HuPBA;MILAB |
Approved |
no |
| Call Number |
Admin @ si @ BGE2012a |
Serial |
2041 |
| Permanent link to this record |
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| Author |
Mohammad Ali Bagheri; Qigang Gao; Sergio Escalera |
| Title |
Error Correcting Output Codes for multiclass classification: Application to two image vision problems |
Type |
Conference Article |
| Year |
2012 |
Publication |
16th symposium on Artificial Intelligence & Signal Processing |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
508-513 |
| Keywords |
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| Abstract |
Error-correcting output codes (ECOC) represents a powerful framework to deal with multiclass classification problems based on combining binary classifiers. The key factor affecting the performance of ECOC methods is the independence of binary classifiers, without which the ECOC method would be ineffective. In spite of its ability on classification of problems with relatively large number of classes, it has been applied in few real world problems. In this paper, we investigate the behavior of the ECOC approach on two image vision problems: logo recognition and shape classification using Decision Tree and AdaBoost as the base learners. The results show that the ECOC method can be used to improve the classification performance in comparison with the classical multiclass approaches. |
| Address |
Shiraz, Iran |
| Corporate Author |
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Thesis |
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| Publisher |
IEEE Xplore |
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 |
978-1-4673-1478-7 |
Medium |
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| Area |
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Expedition |
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Conference |
AISP |
| Notes |
HuPBA;MILAB |
Approved |
no |
| Call Number |
Admin @ si @ BGE2012b |
Serial |
2042 |
| Permanent link to this record |
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| Author |
Dimosthenis Karatzas;Ch. Lioutas |
| Title |
Software Package Development for Electron Diffraction Image Analysis |
Type |
Conference Article |
| Year |
1998 |
Publication |
Proceedings of the XIV Solid State Physics National Conference |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
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| Abstract |
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| Address |
Ioannina, Greece |
| 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 Editor |
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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 |
DAG |
Approved |
no |
| Call Number |
IAM @ iam @ KaL1998 |
Serial |
2045 |
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| Author |
Albert Gordo; Florent Perronnin; Ernest Valveny |
| Title |
Document classification using multiple views |
Type |
Conference Article |
| Year |
2012 |
Publication |
10th IAPR International Workshop on Document Analysis Systems |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
33-37 |
| Keywords |
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| Abstract |
The combination of multiple features or views when representing documents or other kinds of objects usually leads to improved results in classification (and retrieval) tasks. Most systems assume that those views will be available both at training and test time. However, some views may be too `expensive' to be available at test time. In this paper, we consider the use of Canonical Correlation Analysis to leverage `expensive' views that are available only at training time. Experimental results show that this information may significantly improve the results in a classification task. |
| Address |
Australia |
| Corporate Author |
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Thesis |
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| Publisher |
IEEE Computer Society Washington |
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 |
978-0-7695-4661-2 |
Medium |
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| Area |
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Expedition |
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Conference |
DAS |
| Notes |
DAG |
Approved |
no |
| Call Number |
Admin @ si @ GPV2012 |
Serial |
2049 |
| Permanent link to this record |
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| Author |
Albert Gordo; Jose Antonio Rodriguez; Florent Perronnin; Ernest Valveny |
| Title |
Leveraging category-level labels for instance-level image retrieval |
Type |
Conference Article |
| Year |
2012 |
Publication |
25th 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 |
3045-3052 |
| Keywords |
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| Abstract |
In this article, we focus on the problem of large-scale instance-level image retrieval. For efficiency reasons, it is common to represent an image by a fixed-length descriptor which is subsequently encoded into a small number of bits. We note that most encoding techniques include an unsupervised dimensionality reduction step. Our goal in this work is to learn a better subspace in a supervised manner. We especially raise the following question: “can category-level labels be used to learn such a subspace?” To answer this question, we experiment with four learning techniques: the first one is based on a metric learning framework, the second one on attribute representations, the third one on Canonical Correlation Analysis (CCA) and the fourth one on Joint Subspace and Classifier Learning (JSCL). While the first three approaches have been applied in the past to the image retrieval problem, we believe we are the first to show the usefulness of JSCL in this context. In our experiments, we use ImageNet as a source of category-level labels and report retrieval results on two standard dataseis: INRIA Holidays and the University of Kentucky benchmark. Our experimental study shows that metric learning and attributes do not lead to any significant improvement in retrieval accuracy, as opposed to CCA and JSCL. As an example, we report on Holidays an increase in accuracy from 39.3% to 48.6% with 32-dimensional representations. Overall JSCL is shown to yield the best results. |
| Address |
Providence, Rhode Island |
| Corporate Author |
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Thesis |
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| Publisher |
IEEE Xplore |
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 |
1063-6919 |
ISBN |
978-1-4673-1226-4 |
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 @ GRP2012 |
Serial |
2050 |
| Permanent link to this record |
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| Author |
Francisco Cruz; Oriol Ramos Terrades |
| Title |
Document segmentation using relative location features |
Type |
Conference Article |
| Year |
2012 |
Publication |
21st International Conference on Pattern Recognition |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
1562-1565 |
| Keywords |
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| Abstract |
In this paper we evaluate the use of Relative Location Features (RLF) on a historical document segmentation task, and compare the quality of the results obtained on structured and unstructured documents using RLF and not using them. We prove that using these features improve the final segmentation on documents with a strong structure, while their application on unstructured documents does not show significant improvement. Although this paper is not focused on segmenting unstructured documents, results obtained on a benchmark dataset are equal or even overcome previous results of similar works. |
| Address |
Tsukuba Science City, Japan |
| 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 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 |
ICPR |
| Notes |
DAG |
Approved |
no |
| Call Number |
Admin @ si @ CrR2012 |
Serial |
2051 |
| Permanent link to this record |
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| Author |
Volkmar Frinken; Francisco Zamora; Salvador España; Maria Jose Castro; Andreas Fischer; Horst Bunke |
| Title |
Long-Short Term Memory Neural Networks Language Modeling for Handwriting Recognition |
Type |
Conference Article |
| Year |
2012 |
Publication |
21st International Conference on Pattern Recognition |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
701-704 |
| Keywords |
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| Abstract |
Unconstrained handwritten text recognition systems maximize the combination of two separate probability scores. The first one is the observation probability that indicates how well the returned word sequence matches the input image. The second score is the probability that reflects how likely a word sequence is according to a language model. Current state-of-the-art recognition systems use statistical language models in form of bigram word probabilities. This paper proposes to model the target language by means of a recurrent neural network with long-short term memory cells. Because the network is recurrent, the considered context is not limited to a fixed size especially as the memory cells are designed to deal with long-term dependencies. In a set of experiments conducted on the IAM off-line database we show the superiority of the proposed language model over statistical n-gram models. |
| Address |
Tsukuba Science City, Japan |
| 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 |
1051-4651 |
ISBN |
978-1-4673-2216-4 |
Medium |
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| Area |
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Expedition |
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Conference |
ICPR |
| Notes |
DAG |
Approved |
no |
| Call Number |
Admin @ si @ FZE2012 |
Serial |
2052 |
| Permanent link to this record |
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| Author |
Marçal Rusiñol; Dimosthenis Karatzas; Andrew Bagdanov; Josep Llados |
| Title |
Multipage Document Retrieval by Textual and Visual Representations |
Type |
Conference Article |
| Year |
2012 |
Publication |
21st International Conference on Pattern Recognition |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
521-524 |
| Keywords |
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| Abstract |
In this paper we present a multipage administrative document image retrieval system based on textual and visual representations of document pages. Individual pages are represented by textual or visual information using a bag-of-words framework. Different fusion strategies are evaluated which allow the system to perform multipage document retrieval on the basis of a single page retrieval system. Results are reported on a large dataset of document images sampled from a banking workflow. |
| Address |
Tsukuba Science City, Japan |
| 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 |
1051-4651 |
ISBN |
978-1-4673-2216-4 |
Medium |
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| Area |
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Expedition |
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Conference |
ICPR |
| Notes |
DAG |
Approved |
no |
| Call Number |
Admin @ si @ RKB2012 |
Serial |
2053 |
| Permanent link to this record |
