A course in fuzzy systems and control
A course in fuzzy systems and control
Mining fuzzy association rules in databases
ACM SIGMOD Record
Mining fuzzy association rules for classification problems
Computers and Industrial Engineering
Hierarchical Text Classification and Evaluation
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
An Algorithm for Privacy-Preserving Quantitative Association Rules Mining
DASC '06 Proceedings of the 2nd IEEE International Symposium on Dependable, Autonomic and Secure Computing
A survey of content-based image retrieval with high-level semantics
Pattern Recognition
Extraction and optimization of fuzzy association rules using multi-objective genetic algorithm
Pattern Analysis & Applications
A survey of image classification methods and techniques for improving classification performance
International Journal of Remote Sensing
Constructing Category Hierarchies for Visual Recognition
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part IV
A Study of Hierarchical and Flat Classification of Proteins
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
A survey of hierarchical classification across different application domains
Data Mining and Knowledge Discovery
NPIC: hierarchical synthetic image classification using image search and generic features
CIVR'06 Proceedings of the 5th international conference on Image and Video Retrieval
Mining Multilevel Image Semantics via Hierarchical Classification
IEEE Transactions on Multimedia
IEEE Transactions on Fuzzy Systems
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One of the major challenges in the content-based information retrieval and machine learning techniques is to-build-the-so-called "semantic classifier" which is able to effectively and efficiently classify semantic concepts in a large database. This paper dealt with semantic image classification based on hierarchical Fuzzy Association Rules (FARs) mining in the image database. Intuitively, an association rule is a unique and significant combination of image features and a semantic concept, which determines the degree of correlation between features and concept. The main idea behind this approach is that any image visual concept has some associated features, so that, there are strong correlations between the concepts and their corresponding features. Regardless of the semantic gap, an image concept appears when the corresponding features emerge in an image and vice versa. Specially, this paper's contribution was to propose a novel Fuzzy Association Rule for improving traditional association rules. Moreover, it was concerned with establishing a hierarchical fuzzy rule base in the training phase and setup corresponding fuzzy inference engine in order to classify images in the testing phase. The presented approach was independent from image segmentation and can be applied on multi-label images. Experimental results on a database of 6000 general-purpose images demonstrated the superiority of the proposed algorithm.