PicSOM—content-based image retrieval with self-organizing maps
Pattern Recognition Letters - Selected papers from the 11th scandinavian conference on image analysis
Relevance feedback: a power tool for interactive content-based image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
Clustering of the self-organizing map
IEEE Transactions on Neural Networks
Image Retrieval Using Multiple Evidence Ranking
IEEE Transactions on Knowledge and Data Engineering
Expert Systems with Applications: An International Journal
An expansion and reranking approach for annotation-based image retrieval from Web
Expert Systems with Applications: An International Journal
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The explosive growth of digital image collections on the Web sites is calling for an efficient and intelligent method of browsing, searching, and retrieving images. In this article, an artificial neural network (ANN)-based approach is proposed to explore a promising solution to the Web image retrieval (IR). Compared with other image retrieval methods, this new approach has the following characteristics. First of all, the Content-Based features have been combined with Text-Based features to improve retrieval performance. Instead of solely relying on low-level visual features and high-level concepts, we also take the textual features into consideration, which are automatically extracted from image names, alternative names, page titles, surrounding texts, URLs, etc. Secondly, the Kohonen neural network model is introduced and led into the image retrieval process. Due to its self-organizing property, the cognitive knowledge is learned, accumulated, and solidified during the unsupervised training process. The architecture is presented to illustrate the main conceptual components and mechanism of the proposed image retrieval system. To demonstrate the superiority of the new IR system over other IR systems, the retrieval result of a test example is also given in the article.