MI-SIFT: mirror and inversion invariant generalization for SIFT descriptor

Authors:
Rui Ma;Jian Chen;Zhong Su
Affiliations:
IBM Research - China, Beijing, China;IBM Research - China, Beijing, China;IBM Research - China, Beijing, China
Venue:
Proceedings of the ACM International Conference on Image and Video Retrieval
Year:
2010

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Abstract

The best known Scale-Invariant Feature Transform (SIFT) shows its superior performance in a variety of image processing tasks due to its distinctiveness, invariance to scale, rotation and local geometric distortion. Despite its remarkable performance, SIFT is not invariant to mirror images and grayscale-inverted images. This paper proposes an improved SIFT descriptor named MI-SIFT which keeps the advantages of the standard SIFT and is additionally invariant to mirror images and grayscale-inverted images. MI-SIFT is achieved by combining SIFT histogram bins in an elegant way at slight expense of distinctiveness. Most importantly, MI-SIFT can be applied to mirror-like images and inversion-like images which are abundant in real world. Experiments show that MI-SIFT outperforms the standard SIFT on mirror-like and inversion-like images while achieve comparable performance on other images.