Multiple Texture Image Recognition with Unsupervised Block - based Clustering 


Vol. 9,  No. 3, pp. 327-336, Jun.  2002
10.3745/KIPSTB.2002.9.3.327


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  Abstract

Texture analysis is an important technique in many image understanding areas, such as perception of surface, object, shape and depth. But the previous works are intend to the issue of only texture segment, that is not capable of acquiring recognition information. No unsupervised method is based on the recognition of texture in image. we propose a novel approach for efficient texture image analysis that uses unsupervised learning schemes for the texture recognition. The self-organization neural network for multiple texture image identification is based on block-based clustering and merging. The texture features used are the angle and magnitude in orientation-field that might be different from the sample textures. In order to show the performance of the proposed system, After we have attempted to build a various texture images. The final segmentation is achieved by using efficient edge detection algorithm applying to block-based dilation. The experimental results show that the performance of the system is very successful.

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[IEEE Style]

W. B. Lee and W. H. Kim, "Multiple Texture Image Recognition with Unsupervised Block - based Clustering," The KIPS Transactions:PartB , vol. 9, no. 3, pp. 327-336, 2002. DOI: 10.3745/KIPSTB.2002.9.3.327.

[ACM Style]

Woo Beom Lee and Wook Hyun Kim. 2002. Multiple Texture Image Recognition with Unsupervised Block - based Clustering. The KIPS Transactions:PartB , 9, 3, (2002), 327-336. DOI: 10.3745/KIPSTB.2002.9.3.327.

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