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Locality-sensitive hashing for region-based large-scale image indexing

Locality-sensitive hashing for region-based large-scale image indexing

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In this study, the authors present an efficient method for approximate large-scale image indexing and retrieval. The proposed method is mainly based on the visual content of the image regions. Indeed, regions are obtained by a fuzzy segmentation and they are described using high-frequency sub-band wavelets. Moreover, because of the difficulty in managing a huge amount of data, which is caused by the exponential growth of the processing time, approximate nearest neighbour algorithms are used to improve the retrieval speed. Therefore they adopted locality-sensitive hashing (LSH) for region-based indexing of images. In particular, since LSH performance depends fundamentally on the hash function partitioning the space, they exposed a new function, inspired from the E 8 lattice, that can efficiently be combined with the multi-probe LSH and the query-adaptive LSH . To justify the adopted theoretical choices and to highlight the efficiency of the proposed method, a set of experiments related to the region-based image retrieval are carried out on the challenging ‘Wang’ data set.

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