Journal
JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION
Volume 23, Issue 1, Pages 18-30Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.jvcir.2011.07.011
Keywords
Texture image retrieval; Orthogonal polynomials; Rotation invariant; Gabor wavelet; Contourlet Transform; Multiresolution; Feature extraction; Canberra distance
Funding
- All India Council for Technical Education (AICTE), New Delhi, India [8023/BOR/RID/RPS-127/2008-09]
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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. (C) 2011 Elsevier Inc. All rights reserved.
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