期刊名称：International Journal of Information Technology Convergence and Services (IJITCS)
印刷版ISSN：2231-1939
电子版ISSN：2231-153X
出版年度：2011
卷号：1
期号：5
出版社：AIRCC
摘要：Texture is an important spatial feature which plays a vital role in content based image retrieval. The enormous growth of the internet and the wide use of digital data have increased the need for both efficient image database creation and retrieval procedure. This paper describes a new approach for texture classification by combining statistical texture features of Local Binary Pattern and Texture spectrum. Since most significant information of a texture often appears in the high frequency channels, the features are extracted by the computation of LBP and Texture Spectrum and Legendre Moments. Euclidean distance is used for similarity measurement. The experimental result shows that 97.77% classification accuracy is obtained by the proposed method.
