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Image category recognition and feature matching is an important access to visual information on the level of objects and scenes. So far, most works concerning local feature matching are limited in gray images. Designed for gray images, SIFT has been found to be a robust local invariant feature descriptor. However, much information is ignored since the color is discarded in traditional SIFT, and some mismatching problems happen due to this approach. To achieve a more accurate feature extraction and keypoints matching, more advanced method is desperately needed. In this paper, we present a group of color descriptors, to increase the performance traditional SIFT does in some occasions like outdoor scenes. Besides, a RANSAC algorithm is also analyzed and applied to our research, to leave out those obvious mismatching. RANSAC works well for images with many self-similar structures, which decreases mismatches significantly. Then we improved the method so that it converges in fewer iterations.

A Modified Image Registration Algorithm Based on Color Invariance and RANSAC