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Symmetry 2019, 11(1), 21; https://doi.org/10.3390/sym11010021

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

1
School of Information, Zhejiang University of Finance & Economics, Zhejiang 310018, China
2
Department of Computer Science and Information Engineering, Feng Chia University, Taichung 40724, Taiwan
3
Department of Information Engineering and Computer Science, Providence University, Taichung 43301, Taiwan
*
Author to whom correspondence should be addressed.
Received: 2 December 2018 / Revised: 20 December 2018 / Accepted: 24 December 2018 / Published: 27 December 2018
(This article belongs to the Special Issue Emerging Data Hiding Systems in Image Communications)
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Abstract

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. View Full-Text
Keywords: contest-based image retrieval; block truncation coding; ant colony optimization; vector quantization; feature descriptor contest-based image retrieval; block truncation coding; ant colony optimization; vector quantization; feature descriptor
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Chen, Y.-H.; Chang, C.-C.; Lin, C.-C.; Hsu, C.-Y. Content-Based Color Image Retrieval Using Block Truncation Coding Based on Binary Ant Colony Optimization. Symmetry 2019, 11, 21.

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