Feature extraction and learning using context cue and Rényi entropy based mutual information

TY - GEN

T1 - Feature extraction and learning using context cue and Rényi entropy based mutual information

AU - Pan, Hong

AU - Olsen, Søren Ingvor

AU - Zhu, Yaping

N1 - Conference code: 4

PY - 2015

Y1 - 2015

N2 - Feature extraction and learning play a critical role for visual perception tasks. We focus on improving the robustness of the kernel descriptors (KDES) by embedding context cues and further learning a compact and discriminative feature codebook for feature reduction using Rényi entropy based mutual information. In particular, for feature extraction, we develop a new set of kernel descriptors−Context Kernel Descriptors (CKD), which enhance the original KDES by embedding the spatial context into the descriptors. Context cues contained in the context kernel enforce some degree of spatial consistency, thus improving the robustness of CKD. For feature learning and reduction, we propose a novel codebook learning method, based on a Rényi quadratic entropy based mutual information measure called Cauchy-Schwarz Quadratic Mutual Information (CSQMI), to learn a compact and discriminative CKD codebook. Projecting the original full-dimensional CKD onto the codebook, we reduce the dimensionality of CKD while preserving its discriminability. Moreover, 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 verify the effectiveness of our method on several public image benchmark datasets such as YaleB, Caltech-101 and CIFAR-10, as well as a challenging chicken feet dataset of our own. Experimental results show that our method has promising potential for visual object recognition and detection applications.

AB - Feature extraction and learning play a critical role for visual perception tasks. We focus on improving the robustness of the kernel descriptors (KDES) by embedding context cues and further learning a compact and discriminative feature codebook for feature reduction using Rényi entropy based mutual information. In particular, for feature extraction, we develop a new set of kernel descriptors−Context Kernel Descriptors (CKD), which enhance the original KDES by embedding the spatial context into the descriptors. Context cues contained in the context kernel enforce some degree of spatial consistency, thus improving the robustness of CKD. For feature learning and reduction, we propose a novel codebook learning method, based on a Rényi quadratic entropy based mutual information measure called Cauchy-Schwarz Quadratic Mutual Information (CSQMI), to learn a compact and discriminative CKD codebook. Projecting the original full-dimensional CKD onto the codebook, we reduce the dimensionality of CKD while preserving its discriminability. Moreover, 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 verify the effectiveness of our method on several public image benchmark datasets such as YaleB, Caltech-101 and CIFAR-10, as well as a challenging chicken feet dataset of our own. Experimental results show that our method has promising potential for visual object recognition and detection applications.

U2 - 10.1007/978-3-319-27677-9_5

DO - 10.1007/978-3-319-27677-9_5

M3 - Article in proceedings

SN - 978-3-319-27676-2

T3 - Lecture notes in computer science

SP - 69

EP - 88

BT - Pattern recognition: applications and methods

A2 - Fred, Ana

A2 - De Marsico, Maria

A2 - Figueiredo, Mário

PB - Springer

T2 - 4th International Conference on Pattern Recognition: Applications and Methods

Y2 - 10 January 2015 through 12 January 2015

ER -