Multi-Bin search: improved large-scale content-based image retrieval

The challenge of large-scale content-based image retrieval (CBIR) has been recently addressed by many promising approaches. In this work, a new approach that jointly optimizes the search precision and time for large-scale CBIR is presented. This is achieved using binary local image descriptors, such...

Full description

Saved in:
Bibliographic Details
Published in:International journal of multimedia information retrieval 2015-09, Vol.4 (3), p.205-216
Main Authors: Kamel, Abdelrahman, Mahdy, Youssef B., Hussain, Khaled F.
Format: Article
Language:English
Subjects:
Algorithms
Benchmarks
Bins
Codes
Computer Science
Data Mining and Knowledge Discovery
Database Management
Image Processing and Computer Vision
Image retrieval
Information Storage and Retrieval
Information Systems Applications (incl.Internet)
Methods
Multimedia Information Systems
Regular Paper
Retrieval
Search process
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The challenge of large-scale content-based image retrieval (CBIR) has been recently addressed by many promising approaches. In this work, a new approach that jointly optimizes the search precision and time for large-scale CBIR is presented. This is achieved using binary local image descriptors, such as BRIEF or BRISK, along with binary hashing methods, such as Locality-Sensitive Hashing and Spherical Hashing (SH). The proposed approach, named Multi-Bin Search , improves the retrieval precision of binary hashing methods through computing, storing and indexing the nearest neighbor bins for each bin generated from a binary hashing method. Then, the search process does not only search the targeted bin, but also it searches the nearest neighbor bins. To efficiently search inside targeted bins, a fast exhaustive-search equivalent algorithm, inspired by Norm Ordered Matching, has been used. Also, a result reranking step that increases the retrieval precision is introduced, but with a slight increase in search time. Experimental evaluations over famous benchmarking datasets (such as the University of Kentucky Benchmarking, the INRIA Holidays, and the MIRFLICKR-1M) show that the proposed approach highly improves the retrieval precision of the state-of-art binary hashing methods.
ISSN:2192-6611
2192-662X
DOI:10.1007/s13735-014-0061-0