Exploiting global and local features for image retrieval

来源期刊:中南大学学报(英文版)2018年第2期

论文作者:冯林 Li Li(李莉) 吴俊 孙木鑫 刘胜蓝

文章页码:259 - 276

Key words:local binary patterns; hue, saturation, value (HSV) color space; graph fusion; image retrieval

Abstract: Two lines of image representation based on multiple features fusion demonstrate excellent performance in image retrieval. However, there are some problems in both of them: 1) the methods defining directly texture in color space put more emphasis on color than texture feature; 2) the methods extract several features respectively and combine them into a vector, in which bad features may lead to worse performance after combining directly good and bad features. To address the problems above, a novel hybrid framework for color image retrieval through combination of local and global features achieves higher retrieval precision. The bag-of-visual words (BoW) models and color intensity-based local difference patterns (CILDP) are exploited to capture local and global features of an image. The proposed fusion framework combines the ranking results of BoW and CILDP through graph-based density method. The performance of our proposed framework in terms of average precision on Corel-1K database is 86.26%, and it improves the average precision by approximately 6.68% and 12.53% over CILDP and BoW, respectively. Extensive experiments on different databases demonstrate the effectiveness of the proposed framework for image retrieval.

Cite this article as: LI Li, FENG Lin, WU Jun, SUN Mu-xin, LIU Sheng-lan. Exploiting global and local features for image retrieval [J]. Journal of Central South University, 2018, 25(2): 259–276. DOI: https://doi.org/10.1007/s11771- 018-3735-6.

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