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Mining Visual Data

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dc.contributor.author GROZAVU, Nistor
dc.contributor.author ROGOVSCHI, Nicoleta
dc.date.accessioned 2024-01-12T11:26:45Z
dc.date.available 2024-01-12T11:26:45Z
dc.date.issued 2009
dc.identifier.citation GROZAVU, Nistor, ROGOVSCHI, Nicoleta. Mining Visual Data. In: Microelectronics and Computer Science: proc. 6th International Conference, 1-3 Oct. 2009, Chişinău, Republica Moldova, vol. 1, 2009, pp. 349-352. ISBN 978-9975-45-045-4. ISBN 978-9975-45-122-2 (vol. 1). en_US
dc.identifier.isbn 978-9975-45-045-4
dc.identifier.isbn 978-9975-45-122-2
dc.identifier.uri http://repository.utm.md/handle/5014/25815
dc.description.abstract The Internet 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 these annotations, or combined with some clustering results, but they do not allow a rapid browse of these images. In this paper, an image retrieval system is presented, including detailed descriptions of used lwo-SOM approach and a novel interactive learning using user information/response. Also, we show the use of unsupervised learning for images, we do not dispose of the labels, and we will not take into account the corresponding text for the images. The used DataSet contains 17812 images extracted from wikipedia pages, each of which is described by it colors and texture. en_US
dc.language.iso en en_US
dc.publisher Technical University of Moldova en_US
dc.relation.ispartof Proceeding of the 6th International Conference on "Microelectronics and Computer Science", oct.1-3, 2009, Chişinău, Moldova
dc.rights Attribution-NonCommercial-NoDerivs 3.0 United States *
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/us/ *
dc.subject clustering en_US
dc.subject visual data en_US
dc.subject self-organizing maps en_US
dc.subject weighting en_US
dc.title Mining Visual Data en_US
dc.type Article en_US


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