Nested-SIFT for efficient image matching and retrieval

To improve the effectiveness of feature representation and the efficiency of feature matching, we propose a new feature representation, named Nested-SIFT, which utilizes the nesting relationship between SIFT features to group local features. A Nested-SIFT group consists of a bounding feature and several member features covered by the bounding feature. To obtain a compact representation, SimHash strategy is used to compress member features in a Nested-SIFT group into a binary code, and the similarity between two Nested-SIFT groups is efficiently computed by using the binary codes. Extensive experimental results demonstrate the effectiveness and efficiency of our proposed Nested-SIFT approach.