Application of support vector machines in a texture segmentation system based on wavelet features

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dc.contributor.authorNg, B.
dc.contributor.conferenceInternational Conference on Optimization: Techniques and Applications (6th : 2004 : Ballarat, Australia)
dc.contributor.editorRubinov, A.
dc.contributor.editorSniedovich, M.
dc.date.issued2004
dc.description.abstractThis paper investigates the application of Support Vector Machines (SVMs) to segment images based on textural information. The textures are subjected to a wavelets-based feature extraction process with the extracted features used by the SVM for classification. Both binary and multi-class cases are considered, with the latter using a one-against-one approach. Experimental results show an improvement over SVM classification using direct grayscale values as input vectors, while being more robust than alternative classifiers with the same texture features.
dc.description.urihttp://www.ballarat.edu.au/ard/itms/CIAO/ORBNewsletter/ICOTA/Icota_Proceedings/
dc.identifier.citationProceedings of the 6th International Conference on Optimization: Techniques and Applications, 9-11 December 2004, Ballarat, Australia.
dc.identifier.isbn1876851155
dc.identifier.orcidNg, B. [0000-0002-8316-4996]
dc.identifier.urihttp://hdl.handle.net/2440/28480
dc.language.isoen
dc.publisherUniversity of Ballarat
dc.publisher.placeCD-ROM
dc.subjectSupport Vector Machines
dc.subjectWavelet Transform
dc.subjectFeature Extraction
dc.subjectTexture Segmentation
dc.titleApplication of support vector machines in a texture segmentation system based on wavelet features
dc.typeConference paper
pubs.publication-statusPublished

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