Perimeter intercepted length and color t-value as features for nature-image retrieval

Authors:
Yung-Fu Chen;Meng-Hsiun Tsai;Chung-Chuan Cheng;Po-Chou Chan;Yuan-Heng Zhong
Affiliations:
Department of Health Services Management, China Medical University, Taichung, Taiwan;Department of Management of Information Systems, National Chung Hsing University, Taichung, Taiwan;Department of Electrical Engineering, National Chung Hsing University, Taichung, Taiwan;Department of Management of Information Systems, Central Taiwan University of Science and Technology, Taichung, Taiwan;Department of CSIE, Dayeh University, Changhua, Taiwan
Venue:
IEA/AIE'07 Proceedings of the 20th international conference on Industrial, engineering, and other applications of applied intelligent systems
Year:
2007

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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.