Affine Invariant Feature Based on Local Color

LIU Pingping; ZHOU Qiuzhan; ZHAO Hongwei

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Article Navigation > Chinese Journal of Electronics > 2013 > 22(3): 511-516

LIU Pingping, ZHOU Qiuzhan, ZHAO Hongwei, “Affine Invariant Feature Based on Local Color,” Chinese Journal of Electronics, vol. 22, no. 3, pp. 511-516, 2013,

Citation:

LIU Pingping, ZHOU Qiuzhan, ZHAO Hongwei, “Affine Invariant Feature Based on Local Color,” Chinese Journal of Electronics, vol. 22, no. 3, pp. 511-516, 2013,

LIU Pingping, ZHOU Qiuzhan, ZHAO Hongwei, “Affine Invariant Feature Based on Local Color,” Chinese Journal of Electronics, vol. 22, no. 3, pp. 511-516, 2013,

Citation:

LIU Pingping, ZHOU Qiuzhan, ZHAO Hongwei, “Affine Invariant Feature Based on Local Color,” Chinese Journal of Electronics, vol. 22, no. 3, pp. 511-516, 2013,

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Affine Invariant Feature Based on Local Color

1.
School of Computer Science and Technology, Jilin University, Changchun 130012, China;
2.
Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China;
3.
School of Communication Engineering, Jilin University, Changchun 130012, China

Received Date: 2012-04-01
Rev Recd Date: 2012-07-01
Publish Date: 2013-06-15

Abstract

Abstract

Image localization using local features has attracted a lot of attention in recent mobile robots research. A novel, fast local invariant feature in affine transformation is proposed in this article, called AIFLC (Affine invariant feature based on local color). We adopt affine moment invariants to build affine invariant descriptors. Moreover, we use color gradient based center moment instead of original pixel values in order to enhance discriminative power and robustness of descriptor in photometric transformations. Simulation results show that the run time of AIFLC using optimal selection parameters is about 1/3 of classical SIFT algorithm. Using the standard evaluation images and the ones taken by mobile robots, we experimentally demonstrate that the AIFLC outperforms the state-of-art approaches such as SIFT and SURF in terms of image scaling, rotation, viewpoint changing, and blur transformations.
- Local feature,
- Image matching,
- Scalespace,
- Affine moment invariant

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References(22)

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