Discriminative kernel feature extraction and learning for object recognition and detection
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Discriminative kernel feature extraction and learning for object recognition and detection. / Pan, Hong; Olsen, Søren Ingvor; Zhu, Yaping.
Proceedings of the International Conference on Pattern Recognition Applications and Methods. ed. / Maria De Marsico; Mário Figueiredo; Ana Fred. Vol. 1 SCITEPRESS Digital Library, 2015. p. 99-109.Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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TY - GEN
T1 - Discriminative kernel feature extraction and learning for object recognition and detection
AU - Pan, Hong
AU - Olsen, Søren Ingvor
AU - Zhu, Yaping
N1 - Conference code: 4
PY - 2015
Y1 - 2015
N2 - Feature extraction and learning is critical for object recognition and detection. By embedding context cue of image attributes into the kernel descriptors, we propose a set of novel kernel descriptors called context kernel descriptors (CKD). The motivation of CKD is to use the spatial consistency of image attributes or features defined within a neighboring region to improve the robustness of descriptor matching in kernel space. For feature learning, we develop a novel codebook learning method, based on the Cauchy-Schwarz Quadratic Mutual Information (CSQMI) measure, to learn a compact and discriminative CKD codebook from a rich and redundant CKD dictionary. Projecting the original full-dimensional CKD onto the codebook, we reduce the dimensionality of CKD without losing its discriminability. CSQMI derived from Rényi quadratic entropy can be efficiently estimated using a Parzen window estimator even in high-dimensional space. In addition, the latent connection between Rényi quadratic entropy and the mapping data in kernel feature space further facilitates us to capture the geometric structure as well as the information about the underlying labels of the CKD using CSQMI. Thus the resulting codebook and reduced CKD are discriminative. We report superior performance of our algorithm for object recognition on benchmark datasets like Caltech-101 and CIFAR-10, as well as for detection on a challenging chicken feet dataset.
AB - Feature extraction and learning is critical for object recognition and detection. By embedding context cue of image attributes into the kernel descriptors, we propose a set of novel kernel descriptors called context kernel descriptors (CKD). The motivation of CKD is to use the spatial consistency of image attributes or features defined within a neighboring region to improve the robustness of descriptor matching in kernel space. For feature learning, we develop a novel codebook learning method, based on the Cauchy-Schwarz Quadratic Mutual Information (CSQMI) measure, to learn a compact and discriminative CKD codebook from a rich and redundant CKD dictionary. Projecting the original full-dimensional CKD onto the codebook, we reduce the dimensionality of CKD without losing its discriminability. CSQMI derived from Rényi quadratic entropy can be efficiently estimated using a Parzen window estimator even in high-dimensional space. In addition, the latent connection between Rényi quadratic entropy and the mapping data in kernel feature space further facilitates us to capture the geometric structure as well as the information about the underlying labels of the CKD using CSQMI. Thus the resulting codebook and reduced CKD are discriminative. We report superior performance of our algorithm for object recognition on benchmark datasets like Caltech-101 and CIFAR-10, as well as for detection on a challenging chicken feet dataset.
KW - Faculty of Science
KW - Context kernel descriptors, Cauchy-Schwarz Quadratic Mutual Information, Feature extraction and learning, Object recognition and detection
U2 - 10.5220/0005212900990109
DO - 10.5220/0005212900990109
M3 - Article in proceedings
VL - 1
SP - 99
EP - 109
BT - Proceedings of the International Conference on Pattern Recognition Applications and Methods
A2 - De Marsico, Maria
A2 - Figueiredo, Mário
A2 - Fred, Ana
PB - SCITEPRESS Digital Library
T2 - 4th International Conference on Pattern Recognition: Applications and Methods
Y2 - 10 January 2015 through 12 January 2015
ER -
ID: 127884069