Web image retrieval using self-organizing feature map

Qishi Wu, S. Sitharama Iyengar, Mengxia Zhu

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


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.

Original languageEnglish
Pages (from-to)868-875
Number of pages8
JournalJournal of the American Society for Information Science and Technology
Issue number10
StatePublished - Aug 2001


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