Object classification and detection with context kernel descriptors
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Object classification and detection with context kernel descriptors. / Pan, Hong; Olsen, Søren Ingvor; Zhu, Yaping.
Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications: 19th Iberoamerican Congress, CIARP 2014, Puerto Vallarta, Mexico, November 2-5, 2014. Proceedings. ed. / Eduardo Bayro-Corrochano; Edwin Hancock. 2014. p. 827-835 (Lecture notes in computer science, Vol. 8827).Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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TY - GEN
T1 - Object classification and detection with context kernel descriptors
AU - Pan, Hong
AU - Olsen, Søren Ingvor
AU - Zhu, Yaping
N1 - Conference code: 19
PY - 2014
Y1 - 2014
N2 - Context information is important in object representation. By embedding context cue of image attributes into kernel descriptors, we propose a set of novel kernel descriptors called Context Kernel Descriptors (CKD) for object classification and detection. The motivation of CKD is to use spatial consistency of image attributes or features defined within a neighboring region to improve the robustness of descriptor matching in kernel space. For feature selection, Kernel Entropy Component Analysis (KECA) is exploited to learn a subset of discriminative CKD. Different from Kernel Principal Component Analysis (KPCA) that only keeps features contributing mostly to image reconstruction, KECA selects the CKD that contribute mostly to the Rényi entropy of the image. These CKD are discriminative as they relate to the density distribution of the histogram of image attributes. We report superior performance of CKD for object classification on the CIFAR-10 dataset, and for detection on a challenging chicken feet dataset.
AB - Context information is important in object representation. By embedding context cue of image attributes into kernel descriptors, we propose a set of novel kernel descriptors called Context Kernel Descriptors (CKD) for object classification and detection. The motivation of CKD is to use spatial consistency of image attributes or features defined within a neighboring region to improve the robustness of descriptor matching in kernel space. For feature selection, Kernel Entropy Component Analysis (KECA) is exploited to learn a subset of discriminative CKD. Different from Kernel Principal Component Analysis (KPCA) that only keeps features contributing mostly to image reconstruction, KECA selects the CKD that contribute mostly to the Rényi entropy of the image. These CKD are discriminative as they relate to the density distribution of the histogram of image attributes. We report superior performance of CKD for object classification on the CIFAR-10 dataset, and for detection on a challenging chicken feet dataset.
KW - Faculty of Science
KW - Object classification and detection, Feature selection, Kernel descriptors, Kernel entropy component analysis
U2 - 10.1007/978-3-319-12568-8_100
DO - 10.1007/978-3-319-12568-8_100
M3 - Article in proceedings
SN - 978-3-319-12567-1
T3 - Lecture notes in computer science
SP - 827
EP - 835
BT - Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
A2 - Bayro-Corrochano, Eduardo
A2 - Hancock, Edwin
T2 - Iberoamerican Congress 2014
Y2 - 2 November 2014 through 5 November 2014
ER -
ID: 127191961