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Efficient image representation for object recognition via pivots selection

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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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Author information

Authors and Affiliations

  1. Beijing Key Lab of Traffic Data Analysis and Mining, School of Computer and Information Technology, Beijing Jiaotong University, Beijing, 100044, China

    Bojun Xie, Yi Liu, Hui Zhang & Jian Yu

  2. Key Lab of Machine Learning and Computational Intelligence, College of Mathematics and Computer Science, Hebei University, Baoding, 071000, China

    Bojun Xie & Hui Zhang

Authors
  1. Bojun Xie
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  2. Yi Liu
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  3. Hui Zhang
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  4. Jian Yu
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Corresponding author

Correspondence to Yi Liu.

Additional information

Bojun Xie received the BS degree in computer science and technology, MS degree in computer application from Hebei University, China in 2003 and 2006, respectively. He is currently a PhD candidate in Beijing Jiaotong University China. His interests include machine learning and computer vision.

Yi Liu received BS and PhD degrees from Peking University, China in 2004 and 2009, respectively. His current research interests include reasoning and uncertainty modeling in systems biology, machine learning, information retrieval and 3D geometric processing.

Hui Zhang received the BS degree in information and computing science, MS degree in applied mathematics from Hebei University, China in 2003 and 2006, respectively. He is currently a PhD candidate in Beijing Jiaotong University China. His interests include machine learning and computer vision.

Jian Yu received the BS degree in applied mathematics, MS degree in mathematics, and PhD degree in applied mathematics from Peking University, China in 1991, 1994, and 2000, respectively. He is a professor and head of Institute of Computer Science Beijing Jiaotong University China. His current research interests include fuzzy clustering, pattern recognition, and data mining.

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Xie, B., Liu, Y., Zhang, H. et al. Efficient image representation for object recognition via pivots selection. Front. Comput. Sci. 9, 383–391 (2015). https://doi.org/10.1007/s11704-015-4182-7

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  • DOI: https://doi.org/10.1007/s11704-015-4182-7

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