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Semantic real-world image classification for image retrieval with fuzzy-ART neural network

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Abstract

Image retrieval based on semantic learning has attracted considerable attention in recent years. Analyzing the contents of an image and retrieving corresponding semantics are important in semantic-based image retrieval systems. Region-based image retrieval systems attempt to reduce the gap between high-level semantics and low-level features by representing images at the object level. In this paper, we apply principal component analysis to extract significant region features and then incorporate them into the proposed two-phase fuzzy adaptive resonance theory neural network (Fuzzy-ARTNN) for real-world image content classification. In general, Fuzzy-ARTNN is an unsupervised classifier. 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-ARTNN. Moreover, based on the content analysis by the proposed two-phase Fuzzy-ART, each region in an image is associated with a high-level semantic concept. The proposed system supports both query by keyword(s) with/without region size and query by specified region(s). Experimental results show that the proposed method has high accuracy for semantic-based photograph content analysis and that the results of photograph content analysis are similar to the perception of human eyes. In addition, the semantic-based image retrieval system has a high retrieval rate.

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Acknowledgments

This work was supported by the National Science Council of Taiwan, Republic of China, under grant NSC 96-2221-E-224-070.

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Authors and Affiliations

  1. Graduate School of Engineering Science and Technology, National Yunlin University of Science and Technology, Yunlin, Taiwan

    Hung-Jen Wang

  2. Department of Computer Science and Information Engineering, National Yunlin University of Science and Technology, Yunlin, Taiwan

    Chuan-Yu Chang

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  1. Hung-Jen Wang
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  2. Chuan-Yu Chang
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Correspondence to Chuan-Yu Chang.

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Wang, HJ., Chang, CY. Semantic real-world image classification for image retrieval with fuzzy-ART neural network. Neural Comput & Applic 21, 2137–2151 (2012). https://doi.org/10.1007/s00521-011-0660-0

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