Robust feature detection based on local variation for image retrieval

This paper proposes an interest point detector based on wavelet transform as well as a descriptor based on image variation and log-polar coordinate. Taking advantage of the wavelet properties, the proposed method detects a small number of interest points that are distinctive and robust to the illumination changes, scale changes and affine transform. A new descriptor based on the image variation and log-polar coordinate is proposed to represent the image local shape feature without edge detection. Since the proposed descriptor groups the image variation into various levels and separates the image local region into grids based on log-polar coordinate, it overcomes the problem of textured scenes or ill-defined edge images. Experimental results show that the proposed method achieves better matching accuracy and faster matching speed than those of the SIFT, PCA-SIFT and GLOH with less interest points.