Large Scale Partial-Duplicate Image Retrieval Using Invariance Weight of SIFT and SROA Geometric Consistency

The state-of-the-art image retrieval approaches usually quantize SIFT features into visual words. Researchers proposed the notion of bundled feature which simply employs MSER to bundle SIFT features into groups. In this paper, we propose a large scale partial-duplicate image retrieval scheme using invariance weight of SIFT and SROA geometric consistency based on the bundled feature. We calculate the invariance weight of a SIFT feature by voting the number of the SIFT features in the transformed image space. Considering each bundled feature in polar coordinate system, we propose two consistency parameters: the Scale and Radius consistency parameter, and the Orientation and Angle consistency parameter (SROA geometric consistency). Experiments demonstrate that the invariance weight of SIFT feature can improve the performance of image retrieval, and the SROA geometric constraint is more effective and powerful than the existing geometric constraints for the bundled feature.