A Multi-scale Phase Method for Content Based Image Retrieval

In this paper, we present a method using phase features for content based image retrieval (CBIR). Two related key issues of CBIR are feature extraction and similarity measure. However, most traditional methods treat them respectively and prevent further performance improvement. The method proposed here is based on the multi-scale local phase feature (MLPF) and local weighted phase correlation which combines the above two issues together by phase. And phase data is often locally stable with respect to noise, scale change and common illumination change. Moreover, we implement steerable filters to obtain rotation invariant. Finally, experiments have been conducted on image retrieval to show the effectiveness of the proposed method.