LDAHash: Improved Matching with Smaller Descriptors

—SIFT-like local feature descriptors are ubiquitously employed in such computer vision applications as content-based retrieval, video analysis, copy detection, object recognition, photo-tourism and 3D reconstruction. Feature descriptors can be designed to be invariant to certain classes of photometric and geometric transformations, in particular, affine and intensity scale transformations. However, real transformations that an image can undergo can only be approximately modeled in this way, and thus most descriptors are only approximately invariant in practice. Secondly, descriptors are usually high-dimensional (e.g. SIFT is represented as a 128dimensional vector). In large-scale retrieval and matching problems, this can pose challenges in storing and retrieving descriptor data. We map the descriptor vectors into the Hamming space, in which the Hamming metric is used to compare the resulting representations. This way, we reduce the size of the descriptors by representing them as sho...

• Type: Journal
• Year: 2012
• Where: PAMI
• Authors: Christoph Strecha, Alexander A. Bronstein, Michael M. Bronstein, Pascal Fua
• Keywords: Computer Vision Applications, Geometric Transformations, PAMI 2012, Photo Tourism, Software Engineering