• Journal of the Korean Home Economics Association
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• v.49 no.4
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• pp.93-104
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• 2011

The basic framework of traditional Korean clothing construction consists of circle, square and triangle. This composition principle has been presumed to be associated with the Cheonbu concept of the Dangun era wherein nature and human being are considered united. The Cheonbu concept is represented by circle, square and triangle which constitute Cheonbuin, the images and meanings of heaven. It contains profound philosophy wherein a circle symbolizes heaven and represents number one, a square symbolizes earth and represents the number two, and a triangle symbolizes human beings and represents the number three. Circles, squares and triangles have been used as various symbolic meanings both in the east and west and constitute the framework of Hanbok construction while connoting the Cheonbu concept and symbolism of the Cosmo-tree. From this point of view, the unity of human beings and heaven in Cheonbugyeong is symbolically inherent in Hanbok. Therefore, Hanbok with the basic framework of circles, squares and triangles can be considered a positive creation that created a composition principle of body-nature-clothing.

• Journal of Korean Institute of Industrial Engineers
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• v.26 no.4
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• pp.363-375
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• 2000

In this paper we propose a content-based image retrieval method that can search large image databases efficiently by color, texture, and shape content. Quantized RGB histograms and the dominant triple (hue, saturation, and value), which are extracted from quantized HSV joint histogram in the local image region, are used for representing global/local color information in the image. Entropy and maximum entry from co-occurrence matrices are used for texture information and edge angle histogram is used for representing shape information. Relevance feedback approach, which has coupled proposed features, is used for obtaining better retrieval accuracy. Simulation results illustrate the above method provides 77.5 percent precision rate without relevance feedback and increased precision rate using relevance feedback for overall queries. We also present a new indexing method that supports fast retrieval in large image databases. Tree structures constructed by k-means algorithm, along with the idea of triangle inequality, eliminate candidate images for similarity calculation between query image and each database image. We find that the proposed method reduces calculation up to average 92.9 percent of the images from direct comparison.