A multiresolution approach for rotation invariant texture image retrieval with orthogonal polynomials model

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• 作者：R. Krishnamoorthi  ; ;   ; ^{rkrish26@hotmail.com} ; S. Sathiya devi ;   ; ^{sathyadevi_s@yahoo.com}
• 关键词：Texture image retrieval ; Orthogonal polynomials ; Rotation invariant ; Gabor wavelet ; Contourlet Transform ; Multiresolution ; Feature extraction ; Canberra distance
• 刊名：Journal of Visual Communication and Image Representation
• 出版年：2012
• 出版时间：January 2012
• 年：2012
• 卷：23
• 期：1
• 页码：18-30
• 全文大小：2051 K

文摘

In this paper, a simple and an efficient Content Based Image Retrieval which is based on orthogonal polynomials model is presented. This model is built with a set of carefully chosen orthogonal polynomials and is used to extract the low level texture features present in the image under analysis. The orthogonal polynomials model coefficients are reordered into multiresolution subband like structure. Simple statistical and perceptual properties are derived from the subband coefficients to represent the texture features and these features form a feature vector. The efficiency of the proposed feature vector extraction for texture image retrieval is experimented on the standard Brodatz and MIT¡¯s VisTex texture database images with the Canberra distance measure. The proposed method is compared with other existing retrieval schemes such as Discrete Cosine Transformation (DCT) based multiresolution subbands, Gabor wavelet and Contourlet Transform based retrieval schemes and is found to outperform the existing schemes with less computational cost.