NDLI: Mining visual web knowledge utilizing multiple classifier architecture

Content Provider	IEEE Xplore Digital Library
Author	Saad, A.A. Ismail, M.A. Basiouny, S.A. Ramadan, A.A.
Copyright Year	2010
Description	Author affiliation: Computer and Systems Engineering Department, Faculty of Engineering, Alexandria University, Alexandria 21544, Egypt (Saad, A.A.; Ismail, M.A.; Basiouny, S.A.; Ramadan, A.A.)
Abstract	Inspite of the huge amounts of image data on the web, mining image data from the web is paid less attention than mining text data, since treating the semantics of images is much more difficult. This paper introduces a new system to mine visual knowledge on the web that aims to build a Domain Oriented Image Directory by using the Earth Mover's Distance and Color signatures. Instead of using a flat classifier to combine text and image classification, the system suggests dividing the classification task into smaller classification problems corresponding to the branches in the classification hierarchy. Thus a multiple classifier system is presented. This paper illustrates the suggested system and discusses each of its components. Extensive experiments were conducted to test the system and also to compare it with commercial search engines. By the experiments we show that the proposed system accuracy outperforms the mostly used commercial search engines.
Starting Page	598
Ending Page	603
File Size	264944
Page Count	6
File Format	PDF
ISBN	9781424481347
e-ISBN	9781424481361
DOI	10.1109/ISDA.2010.5687198
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-11-29
Publisher Place	Egypt
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Earth Mover's Distance Decision Tree classifier Content based Image Retrieval (CBIR) K Nearest Neighbor Classifier Classification algorithms Training Image color analysis Color signature Web pages Search engines Naïve Bayes Classifier Classification tree analysis Testing
Content Type	Text
Resource Type	Article

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