On the interoperability of local descriptors compression

There are a number of component technologies that are useful for visual search, including format of visual descriptors, descriptor extraction process, as well as indexing, and matching algorithms. As a minimum, the format of descriptors as well as parts of their extraction process should be defined to ensure interoperability. In this paper, we study the problem of interoperability among compressed local descriptors at different bit-rates; that is, allowing effective and efficient comparison of compact descriptors, which is fundamentally important to mobile visual search applications. We propose to combine feature transform and multi-stage vector quantization to implement the interoperability of compact local descriptors. First, an orthogonal transform (e.g. Principle component analysis, PCA) is employed to eliminate the correlation between local feature dimensions, which improves the performance of compressed domain descriptor matching with the well-aligned distance computing of sorted important features in transform space. Second, a multi-stage vector quantization (MSVQ) is applied to generate compact codes for local descriptors. At light quantization tables, MSVQ takes advantage of the transform domain features to properly allocate different budgets to each group of transformed feature dimensions, respectively. The interoperability between compressed descriptors at different bit rates can be achieved by the descriptors' fast matching in the orthogonal feature space. In other words, descriptor decoding into the original feature space (SIFT space) is unnecessary, as the distance can be calculated by pre-computed lookup tables. In particular, such efficient matching in transform domain is significant for large-scale visual search. Over a set of benchmark datasets, we have reported superior performance over state-of-the-arts.