Conference Paper

An Image Retrieval System Based on Colors and Shapes of Objects.

Department of Management Information Systems, Chaoyang University of Technology, Taiwan; Department of Computer Science and Information Engineering, Dayeh University, Taiwan; Department of Management Information Systems, National Chung Hsing University, Taiwan
DOI: 10.1007/11801603_140 In proceeding of: PRICAI 2006: Trends in Artificial Intelligence, 9th Pacific Rim International Conference on Artificial Intelligence, Guilin, China, August 7-11, 2006, Proceedings
Source: DBLP

ABSTRACT This paper proposes a context-based image retrieval method, called color-shape based method (CSBM), based on the colors and shapes of objects in an image. The proposed method characterizes the shape of an object by the object's area and the intercepted lengths obtained by intercepting the object perimeter by eight lines with different orientations passing through the object center. The experimental results show that CSBM provides a better performance than FCH and CCH. Besides, it is insensitive to translation, rotation, distortion, scale, and hue variations, but impressionable to contrast and noise variations.

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    ABSTRACT: This paper proposes a context-based image retrieval system based on color, area, and perimeter intercepted lengths of segmented objects in an image. It characterizes the shape of an object by its area and the intercepted lengths obtained by intercepting the object perimeter by eight lines with different orientations passing through the object center, and the object color by its mean and standard deviation (STD). Recently, we reported that the color-shape based method (CSBM) is better than conventional color histogram (CCH) and fuzzy color histogram (FCH) in retrieving computer-generated images. However, its performance is only fair in the retrieval of natural images. For CSBM, object color is treated as uniform by reducing the number of colors in an image to only 27 colors. In this paper, we improve the performance by representing the color features of an object with its mean and STD. During the image retrieval stage, t-value is calculated based on the color features of two images, one in the query and the other in the database. The result shows that the proposed method achieves better performance in retrieving natural images compared to CCH, FCH, and CSBM. In the future, the proposed technique will be applied for the retrieval of digitized museum artifacts.
    07/2007: pages 185-194;

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