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A content-based image retrieval system based on unsupervised topological learning
Abstract Internet offers to its users an ever-increInternet offers to its users an ever-increasing number of information. Among those, the multimodal data (images, text, video, sound) are widely requested by users, and there is a strong need for effective ways to process and to manage it, respectively. Most of existed algorithms/frameworks are doing only images annotations and the search is doing by this annotations, or combined with some clustering results, but most of them do not allow a quick browsing of these images. Even if the search is very quickly, but if the number of images is very large, the system must give the possibility to the user to browse this data. In this paper, an image retrieval system is presented, including detailed descriptions of used lwo-SOM (local weighting observations Self-Organizing Map) approach and a new interactive learning process using user information/response. Also, we show the use of unsupervised learning on an images dataset, we do not dispose of the labels, and we will not take into account the corresponding text for the images. The used real dataset contains 17812 images extracted from wikipedia pages, each of which is characterized by its color and texture.is characterized by its color and texture.
Abstractsub Internet offers to its users an ever-increInternet offers to its users an ever-increasing number of information. Among those, the multimodal data (images, text, video, sound) are widely requested by users, and there is a strong need for effective ways to process and to manage it, respectively. Most of existed algorithms/frameworks are doing only images annotations and the search is doing by this annotations, or combined with some clustering results, but most of them do not allow a quick browsing of these images. Even if the search is very quickly, but if the number of images is very large, the system must give the possibility to the user to browse this data. In this paper, an image retrieval system is presented, including detailed descriptions of used lwo-SOM (local weighting observations Self-Organizing Map) approach and a new interactive learning process using user information/response. Also, we show the use of unsupervised learning on an images dataset, we do not dispose of the labels, and we will not take into account the corresponding text for the images. The used real dataset contains 17812 images extracted from wikipedia pages, each of which is characterized by its color and texture.is characterized by its color and texture.
Bibtextype inproceedings  +
Has author Rogovschi N. + , Grozavu N. +
Has extra keyword Clustering + , Clustering results + , Content based image retrieval + , Content-based image retrieval system + , Dataset + , Image retrieval systems + , Interactive learning + , Multi-modal data + , Self organizing + , Topological learning + , Wikipedia + , Clustering algorithms + , Conformal mapping + , Content based retrieval + , Data mining + , Information management + , Information retrieval + , Information technology + , Self organizing maps + , Topology + , Search engine +
Has keyword Clustering + , Content-based image retrieval + , Self-organizing maps + , Topological learning +
Isbn 9788988678312  +
Language English +
Number of citations by publication 0  +
Number of references by publication 0  +
Pages 389–393  +
Published in Proc. - 6th Intl. Conference on Advanced Information Management and Service, IMS2010, with ICMIA2010 - 2nd International Conference on Data Mining and Intelligent Information Technology Applications +
Title A content-based image retrieval system based on unsupervised topological learning +
Type conference paper  +
Year 2010 +
Creation dateThis property is a special property in this wiki. 6 November 2014 12:04:46  +
Categories Publications without license parameter  + , Publications without DOI parameter  + , Publications without remote mirror parameter  + , Publications without archive mirror parameter  + , Publications without paywall mirror parameter  + , Conference papers  + , Publications without references parameter  + , Publications  +
Modification dateThis property is a special property in this wiki. 6 November 2014 12:04:46  +
DateThis property is a special property in this wiki. 2010  +
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A content-based image retrieval system based on unsupervised topological learning + Title
 

 

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