C4.5: programs for machine learning
C4.5: programs for machine learning
A simple but powerful heuristic method for generating fuzzy rules from numerical data
Fuzzy Sets and Systems
Shape measures for content based image retrieval: a comparison
Information Processing and Management: an International Journal
Machine Learning
k-means: a new generalized k-means clustering algorithm
Pattern Recognition Letters
Image retrieval system based on color-complexity and color-spatial features
Journal of Systems and Software
Locality preserving clustering for image database
Proceedings of the 12th annual ACM international conference on Multimedia
Content-based multimedia information retrieval: State of the art and challenges
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
A survey of content-based image retrieval with high-level semantics
Pattern Recognition
A modified Gabor function for content based image retrieval
Pattern Recognition Letters
Engineering Applications of Artificial Intelligence
A color image segmentation approach for content-based image retrieval
Pattern Recognition
Increasing the discrimination power of the co-occurrence matrix-based features
Pattern Recognition
Editorial: Data warehouse and knowledge discovery (DAWAK'05)
Data & Knowledge Engineering
Fuzzy classifier design using genetic algorithms
Pattern Recognition
Long-Term Cross-Session Relevance Feedback Using Virtual Features
IEEE Transactions on Knowledge and Data Engineering
Conceptual modeling rules extracting for data streams
Knowledge-Based Systems
Advanced Information Retrieval
Electronic Notes in Theoretical Computer Science (ENTCS)
A smart content-based image retrieval system based on color and texture feature
Image and Vision Computing
Short communication: Data mining based intelligent analysis of threatening e-mail
Knowledge-Based Systems
Wavelet correlogram: A new approach for image indexing and retrieval
Pattern Recognition
Data mining for exploring hidden patterns between KM and its performance
Knowledge-Based Systems
Rule extraction from trained support vector machines
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
IEEE Transactions on Multimedia
Image Retrieval With Relevance Feedback Based on Graph-Theoretic Region Correspondence Estimation
IEEE Transactions on Multimedia
On the efficient evaluation of probabilistic similarity functions for image retrieval
IEEE Transactions on Information Theory
Cascade ARTMAP: integrating neural computation and symbolic knowledge processing
IEEE Transactions on Neural Networks
Integrating wavelets with clustering and indexing for effective content-based image retrieval
Knowledge-Based Systems
A rule-based intelligent method for fault diagnosis of rotating machinery
Knowledge-Based Systems
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The present paper introduces an image retrieval framework based on a rule base system. The proposed framework makes use of color and texture features, respectively called color co-occurrence matrix (CCM) and difference between pixels of scan pattern (DBPSP). These features are used to perform the image mining for acquiring clustering knowledge from a large empirical images database. Irrelevance between images of the same cluster is precisely considered in the proposed framework through a relevance feedback phase followed by a novel clustering refinement model. The images and their corresponding classes pass to a rule base system for extracting a set of accurate rules. These rules are pruning and may reduce the dimensionality of the extracted features. The advantage of the proposed framework is reflected in the retrieval process, which is limited to the images in the class of rule matched with the query image features. Experiments show that the proposed model achieves a very good performance in terms of the average precision, recall and retrieval time compared with other models.