Abstract
Color descriptors of an image are the most widely used visual features in content-based image retrieval systems. In this study, we present a novel color-based image retrieval framework by integrating color space quantization and feature coding. Although color features have advantages such as robustness and simple extraction, direct processing of the abundant amount of color information in an RGB image is a challenging task. To overcome this problem, a color space clustering quantization algorithm is proposed to obtain the clustering color space (CCS) by clustering the CIE1976L*a*b* space into 256 distinct colors, which adequately accommodate human visual perception. In addition, a new feature coding method called feature-to-character coding (FCC) is proposed to encode the block-based main color features into character codes. In this method, images are represented by character codes that contribute to efficiently building an inverted index by using color features and by utilizing text-based search engines. Benefiting from its high-efficiency computation, the proposed framework can also be applied to large-scale web image retrieval. The experimental results demonstrate that the proposed system can produce a significant augmentation in performance when compared to blockbased main color image retrieval systems that utilize the traditional HSV(Hue, Saturation, Value) quantization method.
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Acknowledgements
This work was supported in part by the National Natural Science Foundation of China (Grant No. 61370149), in part by the Fundamental Research Funds for the Central Universities (ZYGX2013J083), and in part by the Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry.
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Le Dong received the PhD degree in electronic engineering and computer science from Queen Mary, University of London, UK in 2009. She is an associate professor in University of Electronic Science and Technology of China, China. Her research interests include computer vision, big data analysis, and biologically inspired system.
Wenpu Dong is an undergraduate majoring in computer science and technology at University of Electronic Science and Technology of China, China. His research interest is mainly on image retrieval.
Ning Feng is a PhD student majoring in computer science and technology at University of Electronic Science and Technology of China, China. His research interests are computer vision and image segmentation.
Mengdie Mao is an undergraduate majoring in computer science and technology at University of Electronic Science and Technology of China, China. Her research interest is mainly on image retrieval and deep learning.
Long Chen received his master degree in University of Electronic Science and Technology of China, China in 2013. He is now working at Chengdu FunMi Technology Company. His research interests are computer vision and image retrieval.
Gaipeng Kong is an undergraduate majoring in computer science and technology at University of Electronic Science and Technology of China, China. Her research interest is mainly on image retrieval.
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Dong, L., Dong, W., Feng, N. et al. Color space quantization-based clustering for image retrieval. Front. Comput. Sci. 11, 1023–1035 (2017). https://doi.org/10.1007/s11704-016-5538-y
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DOI: https://doi.org/10.1007/s11704-016-5538-y