Semantic image retrieval with fuzzy-ART

Analyzing the contents of an image and retrieving corresponding semantics are important in semantic-based image retrieval system. In this paper, we apply the principal component analysis (PCA) to extract significant image features and then incorporated them with the proposed Two-phase Fuzzy Adaptive Resonance Theory Neural Network (Fuzzy-ARTNN) for image content classification. In general, Fuzzy-ARTNN is an unsupervised neural network. Meanwhile, the training patterns in image content analysis are labeled with corresponding categories. This category information is useful for supervised learning. Thus, a supervised learning mechanism is adopted to label the category of the cluster centers derived by the Fuzzy-ARTNN. Moreover, the semantic information is used for real-world image retrieval. Experimental results show that the proposed method has a high accuracy for semantic-based photograph content analysis, and the result of photograph content analysis is similar to perception of the human eyes. In addition, the accuracy of the region-based image retrieval is improved.