  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.
