Abstract
Nowadays most web pages contain both text and images. Nevertheless, search engines index documents based on their disseminated content or their meta-tags only. Although many search engines offer image search, this service is based over textual information filtering and retrieval. Thus, in order to facilitate effective search for images on the web, text analysis and image processing must work in complement. This paper presents an enhanced information fusion version of the meta-search engine proposed in [1], which utilizes up to 9 known search engines simultaneously for content information retrieval while 3 of them can be used for image processing in parallel. In particular this proposed meta-search engine is combined with fuzzy logic rules and a neural network in order to provide an additional search service for human photos in the web.
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© 2004 Springer-Verlag Berlin Heidelberg
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Anagnostopoulos, I., Anagnostopoulos, C., Kouzas, G., Dimitrios, V. (2004). Precise Photo Retrieval on the Web with a Fuzzy Logic\Neural Network-Based Meta-search Engine. In: Vouros, G.A., Panayiotopoulos, T. (eds) Methods and Applications of Artificial Intelligence. SETN 2004. Lecture Notes in Computer Science(), vol 3025. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24674-9_6
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DOI: https://doi.org/10.1007/978-3-540-24674-9_6
Publisher Name: Springer, Berlin, Heidelberg
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