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Efficient image representation for object recognition via pivots selection |
Bojun XIE1,2,Yi LIU1,*( ),Hui ZHANG1,2,Jian YU1 |
1. Beijing Key Lab of Traffic Data Analysis and Mining, School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China
2. Key Lab of Machine Learning and Computational Intelligence, College of Mathematics and Computer Science, Hebei University, Baoding 071000, China |
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Abstract Patch-level features are essential for achieving good performance in computer vision tasks. Besides wellknown pre-defined patch-level descriptors such as scaleinvariant feature transform (SIFT) and histogram of oriented gradient (HOG), the kernel descriptor (KD) method [1] offers a new way to “grow-up” features from a match-kernel defined over image patch pairs using kernel principal component analysis (KPCA) and yields impressive results. In this paper, we present efficient kernel descriptor (EKD) and efficient hierarchical kernel descriptor (EHKD), which are built upon incomplete Cholesky decomposition. EKD automatically selects a small number of pivot features for generating patch-level features to achieve better computational efficiency. EHKD recursively applies EKD to form image-level features layer-by-layer. Perhaps due to parsimony, we find surprisingly that the EKD and EHKD approaches achieved competitive results on several public datasets compared with other state-of-the-art methods, at an improved efficiency over KD.
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| Keywords
efficient kernel descriptor
efficient hierarchical kernel descriptor
incomplete Cholesky decomposition
patch-level features
image-level features
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Corresponding Author(s):
Yi LIU
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Issue Date: 18 May 2015
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