[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.3837/tiis.2019.10.021

Region Division for Large-scale Image Retrieval

Rao, Yunbo (School of Information and Software Engineering, University of Electronic Science and Technology of China)
Liu, Wei (School of Information and Software Engineering, University of Electronic Science and Technology of China)

Publication Information

KSII Transactions on Internet and Information Systems (TIIS) / v.13, no.10, 2019 , pp. 5197-5218 More about this Journal

Abstract

Large-scale retrieval algorithm is problem for visual analyses applications, along its research track. In this paper, we propose a high-efficiency region division-based image retrieve approaches, which fuse low-level local color histogram feature and texture feature. A novel image region division is proposed to roughly mimic the location distribution of image color and deal with the color histogram failing to describe spatial information. Furthermore, for optimizing our region division retrieval method, an image descriptor combining local color histogram and Gabor texture features with reduced feature dimensions are developed. Moreover, we propose an extended Canberra distance method for images similarity measure to increase the fault-tolerant ability of the whole large-scale image retrieval. Extensive experimental results on several benchmark image retrieval databases validate the superiority of the proposed approaches over many recently proposed color-histogram-based and texture-feature-based algorithms.

Keywords

Image retrieval; image color analysis; image texture; image similarity measurement;

Citations & Related Records

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