International Journal of Computer Vision
VisualSEEk: a fully automated content-based image query system
MULTIMEDIA '96 Proceedings of the fourth ACM international conference on Multimedia
Blobworld: Image Segmentation Using Expectation-Maximization and Its Application to Image Querying
IEEE Transactions on Pattern Analysis and Machine Intelligence
The TV-tree: an index structure for high-dimensional data
The VLDB Journal — The International Journal on Very Large Data Bases - Spatial Database Systems
Similarity Indexing with the SS-tree
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
The X-tree: An Index Structure for High-Dimensional Data
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Selecting Bankruptcy Predictors Using a Support Vector Machine Approach
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 6 - Volume 6
Support vector machines for spam categorization
IEEE Transactions on Neural Networks
A pattern recognition and adaptive approach to quality control
WSEAS Transactions on Systems and Control
Improving protein complex classification accuracy using amino acid composition profile
Computers in Biology and Medicine
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Classification technology is essential for fast retrieval in large database. This paper proposes a combining Principal Feature Analysis (PFA) and SVM model to content-based image retrieval. The proposed method is also used to classification similar images from database. Joint HSV histogram and average entropy computed from gray-level co-occurrence matrices in the localized image region is employed as input vectors. PFA is employed to select feature subsets (choosing principal features) eliminated irrelevant factors as used inputs and to determine the optimal parameters of Support Vector Machine. Experimental results show that the proposed model outperforms existing method.