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Project: Learning Similarity Measures Using Neural Network

PEOPLE

Wei Ma, B.S.Manjunath

OBJECTIVE

We have proposed a hybrid neural network algorithm for learning similarity measures in the texture feature space. It achieves the objective of maintaining the topology while reducing the dimensionality, and groups perceptually similar patterns into the same cluster. With Learning similarity, the performance of similar pattern retrievals improves significantly.

SOFTWARE

EXAMPLE    

The following examples show the retrieval performance before (left) and after (right) learning similarity measures. As we can see, the retrieval results better match the human visual perception in terms of texture similarity.

 

Example 1:        

Note: the left block is before learning similarity measures, and the right block is after learning similarity measures

Example 2:       

Example 3:       

PUBLICATIONS

These materials are presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each authors copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.

  1. W.Y.Ma and B.S.Manjunath, Texture features and learning similarity, Proc. IEEE International Conference on Computer Vision and Pattern Recognition, San Francisco, CA, pp. 425-430, June 1996.

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Image Processing and Vision Research Lab, Electrical and Computer Engineering 

Department,  University of California at Santa Barbara, Santa Barbara, CA 93106.