Content-Based Color Image Retrieval Using Block Truncation Coding Based on Binary Ant Colony Optimization

被引:11
作者
Chen, Yan-Hong [1 ]
Chang, Chin-Chen [2 ]
Lin, Chia-Chen [3 ]
Hsu, Cheng-Yi [2 ]
机构
[1] Zhejiang Univ Finance & Econ, Sch Informat, Hangzhou 310018, Zhejiang, Peoples R China
[2] Feng Chia Univ, Dept Comp Sci & Informat Engn, Taichung 40724, Taiwan
[3] Providence Univ, Dept Informat Engn & Comp Sci, Taichung 43301, Taiwan
来源
SYMMETRY-BASEL | 2019年 / 11卷 / 01期
关键词
contest-based image retrieval; block truncation coding; ant colony optimization; vector quantization; feature descriptor;
D O I
10.3390/sym11010021
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
In this paper, we propose a content-based image retrieval (CBIR) approach using color and texture features extracted from block truncation coding based on binary ant colony optimization (BACOBTC). First, we present a near-optimized common bitmap scheme for BTC. Then, we convert the image to two color quantizers and a bitmap image-utilizing BACOBTC. Subsequently, the color and texture features, i.e., the color histogram feature (CHF) and the bit pattern histogram feature (BHF) are extracted to measure the similarity between a query image and the target image in the database and retrieve the desired image. The performance of the proposed approach was compared with several former image-retrieval schemes. The results were evaluated in terms of Precision-Recall and Average Retrieval Rate, and they showed that our approach outperformed the referenced approaches.
引用
收藏
页数:14
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