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Region Division for Large-scale Image Retrieval
  • KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

Region Division for Large-scale Image Retrieval

Vol. 13, No. 10, October 30, 2019
10.3837/tiis.2019.10.021, Download Paper (Free):

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.


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Cite this article

[IEEE Style]
Y. Rao and W. Liu, "Region Division for Large-scale Image Retrieval," KSII Transactions on Internet and Information Systems, vol. 13, no. 10, pp. 5197-5218, 2019. DOI: 10.3837/tiis.2019.10.021.

[ACM Style]
Yunbo Rao and Wei Liu. 2019. Region Division for Large-scale Image Retrieval. KSII Transactions on Internet and Information Systems, 13, 10, (2019), 5197-5218. DOI: 10.3837/tiis.2019.10.021.