Database machines and database management
Database machines and database management
Algorithms for clustering data
Algorithms for clustering data
Texture Features for Browsing and Retrieval of Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Content-Based Image Database Retrieval Using Variances of Gray Level Spatial Dependencies
MINAR '98 Proceedings of the IAPR International Workshop on Multimedia Information Analysis and Retrieval
Automated binary texture feature sets for image retrieval
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 04
Hierarchical partitions for content image retrieval from large-scale database
MLDM'05 Proceedings of the 4th international conference on Machine Learning and Data Mining in Pattern Recognition
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Content-based image retrieval has received significant attention in recent years and many image retrieval systems have been developed based on image contents. In such systems, the well-known features to describe an image content are color, shape and texture. In this paper, we have studied an approach based on clustering of the texture features, aiming both to improve the retrieval performance and to allow users to express their queries easily. To do this, the texture features extracted from images are grouped according to their similarities and then one of them is chosen as a representative for each group. These representatives are then given to users to express their query. Besides the detailed descriptions of clustering process and a summary of results obtained from the experiments, a comparison about statistical texture extraction methods and effects of clustering to them are also presented.