Image semantic classification by using SVM
Data(s) |
01/01/2003
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Resumo |
There exists an enormous gap between low-level visual feature and high-level semantic information, and the accuracy of content-based image classification and retrieval depends greatly on the description of low-level visual features. Taking this into consideration, a novel texture and edge descriptor is proposed in this paper, which can be represented with a histogram. Furthermore, with the incorporation of the color, texture and edge histograms searnlessly, the images are grouped into semantic classes using a support vector machine (SVM). Experiment results show that the combination descriptor is more discriminative than other feature descriptors such as Gabor texture.<br /> |
Identificador | |
Idioma(s) |
eng |
Publicador |
Chinese Academy of Sciences, Institute of Software |
Relação |
http://dro.deakin.edu.au/eserv/DU:30008594/n20030415.pdf http://www.jos.org.cn/ch/reader/create_pdf.aspx?file_no=20031111&flag=1&journal_id=jos |
Direitos |
2003, Journal of Software |
Palavras-Chave | #content-based #image feature descriptor #color #texture #edge #classification #SVM |
Tipo |
Journal Article |