Local feature method robust to compression noise using MSER and magnitudes of Zernike moments

Local feature descriptors based on gradient orientation histogram show good performance even when images contain distortions such as view point change, blur and rotation. However their performance declines significantly when images are compressed using the block DCT based algorithm. Since images and videos are usually encoded to a compressed file format to reduce file size, many image processing applications inevitably treat compressed images. In this paper, we investigate the robustness of Zernike moment against compression noise. In our experiment using the INRIA dataset, we compared the matching results of the descriptors using the magnitudes of Zernike moments with SIFT descriptor in terms of recall vs. 1-precision metric. Magnitudes of Zernike moments provided better matching performance than SIFT when images contain compression noise.