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Bio-inspired invariant visual feature representation based on K-SVD and SURF algorithms

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Published:17 August 2013Publication History

ABSTRACT

In this paper, a bio-inspired invariant visual feature representation method is proposed. A set of Gabor filters with different parameters and global max operation are performed to improve the adaptability to scale and shift changes. In order to extract rotation-invariant features of images, the K-SVD and SURF algorithms are introduced into the traditional HMAX model. Prototypes (feature templates) are learned by the K-SVD algorithm, while the SURF descriptor of patches aims to enhance the rotation invariance. Experimental results on image classification demonstrate the superiority of the proposed feature representation method.

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        • Published in

          cover image ACM Other conferences
          ICIMCS '13: Proceedings of the Fifth International Conference on Internet Multimedia Computing and Service
          August 2013
          419 pages
          ISBN:9781450322522
          DOI:10.1145/2499788
          • Conference Chair:
          • Tat-Seng Chua,
          • General Chairs:
          • Ke Lu,
          • Tao Mei,
          • Xindong Wu

          Copyright © 2013 ACM

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          New York, NY, United States

          Publication History

          • Published: 17 August 2013

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          ICIMCS '13 Paper Acceptance Rate20of94submissions,21%Overall Acceptance Rate163of456submissions,36%

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