Mobile Visual Search Compression With Grassmann Manifold Embedding

With the increasing popularity of mobile phones and tablets, the explosive growth of query-by-capture applications calls for a compact representation of the query image feature. Compact descriptors for visual search (CDVS) is a recently released standard from the ISO/IEC moving pictures experts group, which achieves state-of-the-art performance in the context of image retrieval applications. However, they did not consider the matching characteristics in local space in a large-scale database, which might deteriorate the performance. In this paper, we propose a more compact representation with scale invariant feature transform (SIFT) descriptors for the visual query based on Grassmann manifold. Due to the drastic variations in image content, it is not sufficient to capture all the information using a single transform. To achieve more efficient representations, a SIFT manifold partition tree (SMPT) is initially constructed to divide the large dataset into small groups at multiple scales, which aims at capturing more discriminative information. Grassmann manifold is then applied to prune the SMPT and search for the most distinctive transforms. The experimental results demonstrate that the proposed framework achieves state-of-the-art performance on the standard benchmark CDVS dataset.