Abstract
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-ART) for image content classification to overcome the gap between the low level features and high level semantic concepts. In general, Fuzzy-ART is an unsupervised clustering. 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 added to label the category of the cluster centers derived by the Fuzzy-ART. Moreover, the semantic image content analysis results are used for real-world image retrieval based on the dominant color descriptor (DCD) in MPEG-7. Experimental results show that the proposed method has a high accuracy for semantic-based photograph content analysis, and the accuracy of the region-based image retrieval is highly.
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