Title :
Locality-sensitive hashing for region-based large-scale image indexing
Author :
Gallas, Abir ; Barhoumi, Walid ; Kacem, Neila ; Zagrouba, Ezzeddine
Author_Institution :
RIADI Lab., Manouba Univ., Ariana, Tunisia
Abstract :
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 E8 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.
Keywords :
file organisation; fuzzy set theory; image retrieval; image segmentation; wavelet transforms; LSH; approximate nearest neighbour algorithms; fuzzy segmentation; high-frequency subband wavelets; image regions; locality-sensitive hashing; region-based image retrieval; region-based indexing; region-based large-scale image indexing; visual content;
Journal_Title :
Image Processing, IET
DOI :
10.1049/iet-ipr.2014.0910