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Towards an Images Dataset Processing trough Supervised and Unsupervised Learning

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dc.contributor.author ROGOVSCHI, Nicoleta
dc.contributor.author GROZAVU, Nistor
dc.date.accessioned 2019-10-27T15:42:01Z
dc.date.available 2019-10-27T15:42:01Z
dc.date.issued 2011
dc.identifier.citation ROGOVSCHI, Nicoleta, GROZAVU, Nistor. Towards an Images Dataset Processing trough Supervised and Unsupervised Learning. In: ICNBME-2011. International conference on Nanotechnologies and Biomedical Engineering. German-moldovan workshop on Novel Nanomaterials for Electronic, Photonic and Biomedical Applications: proc. of the intern. conf., July 7-8, 2011. Chişinău, 2011, pp. 434-437. ISBN 978-9975-66-239-0. en_US
dc.identifier.isbn 978-9975-66-239-0
dc.identifier.uri http://repository.utm.md/handle/5014/5435
dc.description.abstract 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 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 we investigate the use of the supervised learning to classify an images dataset and the unsupervised learning to browse the images. In our proposed schema, we used both PCA and LDA to transform the feature space and then to classify the dataset. We used this technique for all five datasets available on the challenge web site of The German Traffic Sign Recognition Benchmark: HOG1, HOG2, HOG3, HueHIst and Haar [7]. Finnaly we used a voting approach to find the consensus for all five partitions. Also, an application to the images browsing is shown using the topological unsupervised learning. en_US
dc.language.iso en en_US
dc.publisher Technical University of Moldova en_US
dc.rights Attribution-NonCommercial-NoDerivs 3.0 United States *
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/us/ *
dc.subject image retrieval en_US
dc.subject topological learning en_US
dc.subject clustering en_US
dc.subject self-organizing maps en_US
dc.title Towards an Images Dataset Processing trough Supervised and Unsupervised Learning en_US
dc.type Article en_US


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