Texture features and learning similarity

W. Y. Ma and B. S. Manjunath
Department of Electrical and Computer Engineering
University of California, Santa Barbara, CA 93106
wei [at] chandra.ece.ucsb.edu, manj [at] surya.ece.ucsb.edu


This paper addresses two important issues related to texture pattern retrieval: feature extraction and similarity search. A Gabor feature representation for textured images is proposed, and its performance in pattern retrieval is evaluated on a large texture image database. These features compare favorably with other existing texture representations. A simple hybrid neural network algorithm is used to learn the similarity by simple clustering in the texture feature space. With learning similarity, the performance of similar pattern retrieval improves significantly. An important aspect of this work is its application to real image data. Texture feature extraction with similarity learning is used to search through large aerial photographs. Feature clustering enables efficient search of the database as our experimental results indicate.
[PDF] [BibTex]
W. Y. Ma and B. S. Manjunath,
IEEE International Conference on Computer Vision and Pattern Recognition, pp. 425-430, San Francisco, CA, Jun. 1996.
Node ID: 246 , DB ID: 40 , VRLID: 35 , Lab: VRL , Target: Proceedings
Subject: [Image Texture] « Look up more