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Mathematical Problems in Engineering
Volume 2016, Article ID 8217250, 12 pages
http://dx.doi.org/10.1155/2016/8217250
Research Article

A Novel Image Retrieval Based on a Combination of Local and Global Histograms of Visual Words

1Department of Computer Engineering, University of Engineering and Technology, Taxila 47050, Pakistan
2Department of Software Engineering, University of Engineering and Technology, Taxila 47050, Pakistan
3Department of Computer Science, University of Engineering and Technology, Taxila 47050, Pakistan
4Department of Computer Engineering, Umm Al-Qura University, Makkah 21421, Saudi Arabia

Received 19 April 2016; Revised 14 June 2016; Accepted 19 June 2016

Academic Editor: Jinyang Liang

Copyright © 2016 Zahid Mehmood et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Content-based image retrieval (CBIR) provides a sustainable solution to retrieve similar images from an image archive. In the last few years, the Bag-of-Visual-Words (BoVW) model gained attention and significantly improved the performance of image retrieval. In the standard BoVW model, an image is represented as an orderless global histogram of visual words by ignoring the spatial layout. The spatial layout of an image carries significant information that can enhance the performance of CBIR. In this paper, we are presenting a novel image representation that is based on a combination of local and global histograms of visual words. The global histogram of visual words is constructed over the whole image, while the local histogram of visual words is constructed over the local rectangular region of the image. The local histogram contains the spatial information about the salient objects. Extensive experiments and comparisons conducted on Corel-A, Caltech-256, and Ground Truth image datasets demonstrate that the proposed image representation increases the performance of image retrieval.