Open AccessLocality‐sensitive hashing for region‐based large‐scale image indexing
Author(s) -
Gallas Abir,
Barhoumi Walid,
Kacem Neila,
Zagrouba Ezzeddine
Publication year - 2015
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2014.0910
Subject(s) - locality sensitive hashing , search engine indexing , hash function , computer science , image retrieval , pattern recognition (psychology) , locality , dynamic perfect hashing , universal hashing , artificial intelligence , image (mathematics) , data mining , hash table , double hashing , linguistics , philosophy , computer security
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.