Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/83720
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dc.contributor.authorDell'Agnello, D.en
dc.contributor.authorCarneiro, G.en
dc.contributor.authorChin, T.en
dc.contributor.authorCastellano, G.en
dc.contributor.authorFanelli, A.en
dc.date.issued2013en
dc.identifier.citationProceedings of the 2013 Joint IFSA World Congress and NAFIPS Annual Meeting, IFSA/NAFIPS 2013, 2013 / W. Pedrycz, M. Z. Reformat (eds.), pp.1439-1444en
dc.identifier.isbn9781479903474en
dc.identifier.urihttp://hdl.handle.net/2440/83720-
dc.description.abstractNowadays the bag-of-visual-words is a very popular approach to perform the task of Visual Object Classification (VOC). Two key phases of VOC are the vocabulary building step, i.e. the construction of a `visual dictionary' including common codewords in the image corpus, and the assignment step, i.e. the encoding of the images by means of these codewords. Hard assignment of image descriptors to visual codewords is commonly used in both steps. However, as only a single visual word is assigned to a given feature descriptor, hard assignment may hamper the characterization of an image in terms of the distribution of visual words, which may lead to poor classification of the images. Conversely, soft assignment can improve classification results, by taking into account the relevance of the feature descriptor to more than one visual word. Fuzzy Set Theory (FST) is a natural way to accomplish soft assignment. In particular, fuzzy clustering can be well applied within the VOC framework. In this paper we investigate the effects of using the well-known Fuzzy C-means algorithm and its kernelized version to create the visual vocabulary and to perform image encoding. Preliminary results on the Pascal VOC data set show that fuzzy clustering can improve the encoding step of VOC. In particular, the use of KFCM provides better classification results than standard FCM and K-means.en
dc.description.statementofresponsibilityDanilo Dell’Agnello and Gustavo Carneiro and Tat-Jun Chin, Giovanna Castellano and Anna Maria Fanellien
dc.language.isoenen
dc.publisherIEEEen
dc.rights©2013 IEEEen
dc.titleFuzzy clustering based encoding for visual object classificationen
dc.typeConference paperen
dc.identifier.rmid0020132816en
dc.contributor.conferenceJoint World Congress on Fuzzy Systems and NAFIPS Annual Meeting (2013 : Edmonton, Canada)en
dc.identifier.doi10.1109/IFSA-NAFIPS.2013.6608613en
dc.publisher.placeUSAen
dc.identifier.pubid17433-
pubs.library.collectionComputer Science publicationsen
pubs.verification-statusVerifieden
pubs.publication-statusPublisheden
dc.identifier.orcidCarneiro, G. [0000-0002-5571-6220]en
Appears in Collections:Computer Science publications

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