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Low-Dimensional Local Self-Similarity Descriptor for Image Matching

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Advances in Automation and Robotics, Vol. 2

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 123))

Abstract

The advanced descriptor, i.e., Local Self-Similarities (LSS), is successfully adopted for object classification and scene recognition. However, it is only invariance against small geometric and photometric transformations. In this paper, two low-dimensional descriptors for extracting distinctive invariant features from interest regions are presented, i.e., PCA and Local Self-Similarities feature based descriptors, namely PCA-LSS and PLSS. They are achieved by applying PCA on LSS feature field and the improved LSS descriptors of normalized patches, respectively. The performance of these proposed PCA-LSS and PLSS descriptors to image matching is studied through extensive experiments on the INRIA Oxford Affine dataset. Empirical results indicate that the proposed PLSS achieves more transformation invariance, significantly outperforms the original LSS, and also outperforms the SIFT.

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Author information

Authors and Affiliations

  1. State Key Laboratory of Advanced Optical Communication Systems and Networks, Department of Electronic Engineering, Shanghai Jiaotong University, Shanghai, 200240, China

    Jingneng Liu & Guihua Zeng

Authors
  1. Jingneng Liu
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  2. Guihua Zeng
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Editors and Affiliations

  1. Information Engineering Research Institute , 100 Continental Dr, 19713, Newark, DE, USA

    Gary Lee

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© 2011 Springer-Verlag Berlin Heidelberg

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Liu, J., Zeng, G. (2011). Low-Dimensional Local Self-Similarity Descriptor for Image Matching. In: Lee, G. (eds) Advances in Automation and Robotics, Vol. 2. Lecture Notes in Electrical Engineering, vol 123. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25646-2_39

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  • DOI: https://doi.org/10.1007/978-3-642-25646-2_39

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-25645-5

  • Online ISBN: 978-3-642-25646-2

  • eBook Packages: EngineeringEngineering (R0)

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