Laplacian embedding and key points topology verification for large scale mobile visual identification

Visual query-by-capture applications call for a compact visual descriptor with minimum descriptor length. Preserving the visual identification performance while minimising the bit rate is a focus of the on-going MPEG7 CDVS (Compact Descriptors for Visual Search) standardisation effort. In this paper we tackle this problem by adopting Laplacian embedding for SIFT feature compression and employing topology verification based on a novel graph cut measure. In contrast to previous feature compression schemes, we approach the problem by finding a Laplacian embedding that preserves the nearest neighbour relations in feature space. Furthermore, we develop an efficient yet effective topology verification (TV) scheme to perform spatial consistency checking. In contrast to previous works on geometric verification, instead of enumerating all possible combinations of coordinate alignments of an image pair, this TV solution verifies possibly misaligned coordinate sets with a learning method which acquires a proper boundary between the topology representation of matched and non-matched image pairs. Furthermore, this TV solution is invariant to in-plane rotation, scaling and is quite resilient to a range of out-of-plane rotations. The proposed Laplacian embedding and Topological verification scheme are tested with the CDVS dataset and are found to be effective.