Color space quantization-based clustering for image retrieval

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.

Keywords

content-based image retrieval / color space quantization / feature coding / inverted index

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