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Towards image retrieval by texture segmentation with genetic programming

Ciesielski, V, Kurnianan, D and Song, A 2007, 'Towards image retrieval by texture segmentation with genetic programming', in L. Guan (ed.) Proceedings of the IEEE Symposium on Computational Intelligence in Image and Signal Processing, Honolulu, 1-5 April 2007.

Document type: Conference Paper
Collection: Conference Papers

Title Towards image retrieval by texture segmentation with genetic programming
Author(s) Ciesielski, V
Kurnianan, D
Song, A
Year 2007
Conference name IEEE Symposium on Computational Intelligence in Image and Signal Processing
Conference location Honolulu
Conference dates 1-5 April 2007
Proceedings title Proceedings of the IEEE Symposium on Computational Intelligence in Image and Signal Processing
Editor(s) L. Guan
Publisher IEEE
Place of publication Honolulu
Abstract This paper examines the feasibility of an approach to image retrieval from a heterogeneous collection based on texture. For each texture of interest (T), a T-vs-other classifier is evolved for small n times n windows using genetic programming. The classifier is then used to segment the images in the collection. If there is a significant contiguous area of T in an image, it is considered to contain that texture for retrieval purposes. We have experimented with sky and grass textures in the Corel Volume 12 image set. Experiments with a single image indicate that classifiers for the two textures can be learned to a high accuracy. Experiments with a test set of 714 Corel images gave a retrieval accuracy of 84% for both sky and grass textures. These results suggest that the use of texture could enhance retrieval accuracy in content based image retrieval systems.
Subjects Neural, Evolutionary and Fuzzy Computation
Copyright notice © 2007 IEEE
 
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Created: Wed, 08 Apr 2009, 09:42:32 EST by Catalyst Administrator