Records |
Author |
Emanuel Indermühle; Volkmar Frinken; Horst Bunke |
Title |
Mode Detection in Online Handwritten Documents using BLSTM Neural Networks |
Type |
Conference Article |
Year |
2012 |
Publication |
13th International Conference on Frontiers in Handwriting Recognition |
Abbreviated Journal |
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Volume |
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Issue |
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Pages |
302-307 |
Keywords |
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Abstract |
Mode detection in online handwritten documents refers to the process of distinguishing different types of contents, such as text, formulas, diagrams, or tables, one from another. In this paper a new approach to mode detection is proposed that uses bidirectional long-short term memory (BLSTM) neural networks. The BLSTM neural network is a novel type of recursive neural network that has been successfully applied in speech and handwriting recognition. In this paper we show that it has the potential to significantly outperform traditional methods for mode detection, which are usually based on stroke classification. As a further advantage over previous approaches, the proposed system is trainable and does not rely on user-defined heuristics. Moreover, it can be easily adapted to new or additional types of modes by just providing the system with new training data. |
Address |
Bari, italy |
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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Series Editor |
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Series Title |
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Series Issue |
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Edition |
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ISSN |
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ISBN |
978-1-4673-2262-1 |
Medium |
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Area |
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Expedition |
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Conference |
ICFHR |
Notes |
DAG |
Approved |
no |
Call Number |
Admin @ si @ IFB2012 |
Serial |
2056 |
Permanent link to this record |
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Author |
Antonio Hernandez; Nadezhda Zlateva; Alexander Marinov; Miguel Reyes; Petia Radeva; Dimo Dimov; Sergio Escalera |
Title |
Graph Cuts Optimization for Multi-Limb Human Segmentation in Depth Maps |
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 |
726-732 |
Keywords |
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Abstract |
We present a generic framework for object segmentation using depth maps based on Random Forest and Graph-cuts theory, and apply it to the segmentation of human limbs in depth maps. First, from a set of random depth features, Random Forest is used to infer a set of label probabilities for each data sample. This vector of probabilities is used as unary term in α-β swap Graph-cuts algorithm. Moreover, depth of spatio-temporal neighboring data points are used as boundary potentials. Results on a new multi-label human depth data set show high performance in terms of segmentation overlapping of the novel methodology compared to classical approaches. |
Address |
Portland; Oregon; June 2013 |
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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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 |
MILAB;HuPBA |
Approved |
no |
Call Number |
Admin @ si @ HZM2012b |
Serial |
2046 |
Permanent link to this record |
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Author |
Antonio Hernandez; Nadezhda Zlateva; Alexander Marinov; Miguel Reyes; Petia Radeva; Dimo Dimov; Sergio Escalera |
Title |
Human Limb Segmentation in Depth Maps based on Spatio-Temporal Graph Cuts Optimization |
Type |
Journal Article |
Year |
2012 |
Publication |
Journal of Ambient Intelligence and Smart Environments |
Abbreviated Journal |
JAISE |
Volume |
4 |
Issue |
6 |
Pages |
535-546 |
Keywords |
Multi-modal vision processing; Random Forest; Graph-cuts; multi-label segmentation; human body segmentation |
Abstract |
We present a framework for object segmentation using depth maps based on Random Forest and Graph-cuts theory, and apply it to the segmentation of human limbs. First, from a set of random depth features, Random Forest is used to infer a set of label probabilities for each data sample. This vector of probabilities is used as unary term in α−β swap Graph-cuts algorithm. Moreover, depth values of spatio-temporal neighboring data points are used as boundary potentials. Results on a new multi-label human depth data set show high performance in terms of segmentation overlapping of the novel methodology compared to classical approaches. |
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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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 |
1876-1364 |
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 |
MILAB;HuPBA |
Approved |
no |
Call Number |
Admin @ si @ HZM2012a |
Serial |
2006 |
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Author |
Xu Hu |
Title |
Real-Time Part Based Models for Object Detection |
Type |
Report |
Year |
2012 |
Publication |
CVC Technical Report |
Abbreviated Journal |
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Volume |
171 |
Issue |
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Pages |
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Keywords |
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Abstract |
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Address |
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Corporate Author |
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Thesis |
Master's thesis |
Publisher |
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Place of Publication |
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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 |
ADAS;ISE |
Approved |
no |
Call Number |
Admin @ si @ Hu2012 |
Serial |
2415 |
Permanent link to this record |
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Author |
Mario Hernandez; Joao Sanchez; Jordi Vitria |
Title |
Selected papers from Iberian Conference on Pattern Recognition and Image Analysis |
Type |
Book Whole |
Year |
2012 |
Publication |
Pattern Recognition |
Abbreviated Journal |
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Volume |
45 |
Issue |
9 |
Pages |
3047-3582 |
Keywords |
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Abstract |
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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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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 |
OR;MV |
Approved |
no |
Call Number |
Admin @ si @ HSV2012 |
Serial |
2069 |
Permanent link to this record |
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Author |
Antonio Hernandez; Miguel Reyes; Victor Ponce; Sergio Escalera |
Title |
GrabCut-Based Human Segmentation in Video Sequences |
Type |
Journal Article |
Year |
2012 |
Publication |
Sensors |
Abbreviated Journal |
SENS |
Volume |
12 |
Issue |
11 |
Pages |
15376-15393 |
Keywords |
segmentation; human pose recovery; GrabCut; GraphCut; Active Appearance Models; Conditional Random Field |
Abstract |
In this paper, we present a fully-automatic Spatio-Temporal GrabCut human segmentation methodology that combines tracking and segmentation. GrabCut initialization is performed by a HOG-based subject detection, face detection, and skin color model. Spatial information is included by Mean Shift clustering whereas temporal coherence is considered by the historical of Gaussian Mixture Models. Moreover, full face and pose recovery is obtained by combining human segmentation with Active Appearance Models and Conditional Random Fields. Results over public datasets and in a new Human Limb dataset show a robust segmentation and recovery of both face and pose using the presented methodology. |
Address |
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Thesis |
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Publisher |
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Place of Publication |
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Language |
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Original Title |
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Series Editor |
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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 |
HuPBA;MILAB |
Approved |
no |
Call Number |
Admin @ si @ HRP2012 |
Serial |
2147 |
Permanent link to this record |
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Author |
Rui Hua; Oriol Pujol; Francesco Ciompi; Marina Alberti; Simone Balocco; J. Mauri; Petia Radeva |
Title |
Stent Strut Detection by Classifying a Wide Set of IVUS Features |
Type |
Conference Article |
Year |
2012 |
Publication |
Computed Assisted Stenting Workshop |
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 |
Nice, France |
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 |
STENT |
Notes |
MILAB;HuPBA |
Approved |
no |
Call Number |
Admin @ si @ HPC2012 |
Serial |
2169 |
Permanent link to this record |
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Author |
Antonio Hernandez; Carlo Gatta; Sergio Escalera; Laura Igual; Victoria Martin-Yuste; Manel Sabate; Petia Radeva |
Title |
Accurate coronary centerline extraction, caliber estimation and catheter detection in angiographies |
Type |
Journal Article |
Year |
2012 |
Publication |
IEEE Transactions on Information Technology in Biomedicine |
Abbreviated Journal |
TITB |
Volume |
16 |
Issue |
6 |
Pages |
1332-1340 |
Keywords |
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Abstract |
Segmentation of coronary arteries in X-Ray angiography is a fundamental tool to evaluate arterial diseases and choose proper coronary treatment. The accurate segmentation of coronary arteries has become an important topic for the registration of different modalities which allows physicians rapid access to different medical imaging information from Computed Tomography (CT) scans or Magnetic Resonance Imaging (MRI). In this paper, we propose an accurate fully automatic algorithm based on Graph-cuts for vessel centerline extraction, caliber estimation, and catheter detection. Vesselness, geodesic paths, and a new multi-scale edgeness map are combined to customize the Graph-cuts approach to the segmentation of tubular structures, by means of a global optimization of the Graph-cuts energy function. Moreover, a novel supervised learning methodology that integrates local and contextual information is proposed for automatic catheter detection. We evaluate the method performance on three datasets coming from different imaging systems. The method performs as good as the expert observer w.r.t. centerline detection and caliber estimation. Moreover, the method discriminates between arteries and catheter with an accuracy of 96.5%, sensitivity of 72%, and precision of 97.4%. |
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 |
1089-7771 |
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 |
MILAB;HuPBA |
Approved |
no |
Call Number |
Admin @ si @ HGE2012 |
Serial |
2141 |
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 |
Michael Holte; Bhaskar Chakraborty; Jordi Gonzalez; Thomas B. Moeslund |
Title |
A Local 3D Motion Descriptor for Multi-View Human Action Recognition from 4D Spatio-Temporal Interest Points |
Type |
Journal Article |
Year |
2012 |
Publication |
IEEE Journal of Selected Topics in Signal Processing |
Abbreviated Journal |
J-STSP |
Volume |
6 |
Issue |
5 |
Pages |
553-565 |
Keywords |
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Abstract |
In this paper, we address the problem of human action recognition in reconstructed 3-D data acquired by multi-camera systems. We contribute to this field by introducing a novel 3-D action recognition approach based on detection of 4-D (3-D space $+$ time) spatio-temporal interest points (STIPs) and local description of 3-D motion features. STIPs are detected in multi-view images and extended to 4-D using 3-D reconstructions of the actors and pixel-to-vertex correspondences of the multi-camera setup. Local 3-D motion descriptors, histogram of optical 3-D flow (HOF3D), are extracted from estimated 3-D optical flow in the neighborhood of each 4-D STIP and made view-invariant. The local HOF3D descriptors are divided using 3-D spatial pyramids to capture and improve the discrimination between arm- and leg-based actions. Based on these pyramids of HOF3D descriptors we build a bag-of-words (BoW) vocabulary of human actions, which is compressed and classified using agglomerative information bottleneck (AIB) and support vector machines (SVMs), respectively. Experiments on the publicly available i3DPost and IXMAS datasets show promising state-of-the-art results and validate the performance and view-invariance of the approach. |
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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 |
1932-4553 |
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 @ HCG2012 |
Serial |
1994 |
Permanent link to this record |
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Author |
Antonio Hernandez; Miguel Angel Bautista; Xavier Perez Sala; Victor Ponce; Xavier Baro; Oriol Pujol; Cecilio Angulo; Sergio Escalera |
Title |
BoVDW: Bag-of-Visual-and-Depth-Words for Gesture 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 |
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Keywords |
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Abstract |
We present a Bag-of-Visual-and-Depth-Words (BoVDW) model for gesture recognition, an extension of the Bag-of-Visual-Words (BoVW) model, that benefits from the multimodal fusion of visual and depth features. State-of-the-art RGB and depth features, including a new proposed depth descriptor, are analysed and combined in a late fusion fashion. The method is integrated in a continuous gesture recognition pipeline, where Dynamic Time Warping (DTW) algorithm is used to perform prior segmentation of gestures. Results of the method in public data sets, within our gesture recognition pipeline, show better performance in comparison to a standard BoVW model. |
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 |
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 |
HuPBA;MV |
Approved |
no |
Call Number |
Admin @ si @ HBP2012 |
Serial |
2122 |
Permanent link to this record |
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Author |
Jaume Gibert; Ernest Valveny; Horst Bunke; Alicia Fornes |
Title |
On the Correlation of Graph Edit Distance and L1 Distance in the Attribute Statistics Embedding Space |
Type |
Conference Article |
Year |
2012 |
Publication |
Structural, Syntactic, and Statistical Pattern Recognition, Joint IAPR International Workshop |
Abbreviated Journal |
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Volume |
7626 |
Issue |
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Pages |
135-143 |
Keywords |
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Abstract |
Graph embeddings in vector spaces aim at assigning a pattern vector to every graph so that the problems of graph classification and clustering can be solved by using data processing algorithms originally developed for statistical feature vectors. An important requirement graph features should fulfil is that they reproduce as much as possible the properties among objects in the graph domain. In particular, it is usually desired that distances between pairs of graphs in the graph domain closely resemble those between their corresponding vectorial representations. In this work, we analyse relations between the edit distance in the graph domain and the L1 distance of the attribute statistics based embedding, for which good classification performance has been reported on various datasets. We show that there is actually a high correlation between the two kinds of distances provided that the corresponding parameter values that account for balancing the weight between node and edge based features are properly selected. |
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Corporate Author |
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Thesis |
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Publisher |
Springer-Berlag, Berlin |
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 |
LNCS |
Series Volume |
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Series Issue |
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Edition |
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ISSN |
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ISBN |
978-3-642-34165-6 |
Medium |
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Area |
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Expedition |
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Conference |
SSPR&SPR |
Notes |
DAG |
Approved |
no |
Call Number |
Admin @ si @ GVB2012c |
Serial |
2167 |
Permanent link to this record |
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Author |
Jaume Gibert; Ernest Valveny; Horst Bunke |
Title |
Feature Selection on Node Statistics Based Embedding of Graphs |
Type |
Journal Article |
Year |
2012 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
Volume |
33 |
Issue |
15 |
Pages |
1980–1990 |
Keywords |
Structural pattern recognition; Graph embedding; Feature ranking; PCA; Graph classification |
Abstract |
Representing a graph with a feature vector is a common way of making statistical machine learning algorithms applicable to the domain of graphs. Such a transition from graphs to vectors is known as graphembedding. A key issue in graphembedding is to select a proper set of features in order to make the vectorial representation of graphs as strong and discriminative as possible. In this article, we propose features that are constructed out of frequencies of node label representatives. We first build a large set of features and then select the most discriminative ones according to different ranking criteria and feature transformation algorithms. On different classification tasks, we experimentally show that only a small significant subset of these features is needed to achieve the same classification rates as competing to state-of-the-art methods. |
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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 |
DAG |
Approved |
no |
Call Number |
Admin @ si @ GVB2012b |
Serial |
1993 |
Permanent link to this record |
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Author |
Jaume Gibert; Ernest Valveny; Horst Bunke |
Title |
Graph Embedding in Vector Spaces by Node Attribute Statistics |
Type |
Journal Article |
Year |
2012 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
Volume |
45 |
Issue |
9 |
Pages |
3072-3083 |
Keywords |
Structural pattern recognition; Graph embedding; Data clustering; Graph classification |
Abstract |
Graph-based representations are of broad use and applicability in pattern recognition. They exhibit, however, a major drawback with regards to the processing tools that are available in their domain. Graphembedding into vectorspaces is a growing field among the structural pattern recognition community which aims at providing a feature vector representation for every graph, and thus enables classical statistical learning machinery to be used on graph-based input patterns. In this work, we propose a novel embedding methodology for graphs with continuous nodeattributes and unattributed edges. The approach presented in this paper is based on statistics of the node labels and the edges between them, based on their similarity to a set of representatives. We specifically deal with an important issue of this methodology, namely, the selection of a suitable set of representatives. In an experimental evaluation, we empirically show the advantages of this novel approach in the context of different classification problems using several databases of graphs. |
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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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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 |
DAG |
Approved |
no |
Call Number |
Admin @ si @ GVB2012a |
Serial |
1992 |
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 |