Rallabandi, Venkateswara Rao and Rallabandi, V. P. Subramanyam (2008) Rotation-invariant texture retrieval using wavelet-based hidden Markov trees. Signal Processing, 88 (10). pp. 2593-2598. ISSN 0165-1684
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Identification Number or DOI: doi: 10.1016/j.sigpro.2008.04.019
In this paper, we present a novel approach for rotation-invariant texture retrieval using multistated wavelet-based hidden Markov trees (MWHMT). We propose a new model to capture statistical dependencies across three independent wavelet subbands. The proposed approach has been applied to CBIR application, rotation-invariant texture retrieval. The feature extraction of the texture is then performed using the signature of the texture, which is generated from the wavelet coefficients of each subband across each scale. We used Kullback–Leibler (KL) distance measure to find the similarity between textures. We have tested our approach for Brodatz texture database and evaluate the retrieval performance in terms of precision and recall. The experimental results show that the proposed method outperforms earlier wavelet-based methods.
|Item Type:||Article (Commonwealth Reporting Category C)|
|Additional Information:||Permanent restricted access to published version due to publisher copyright policy.|
|Uncontrolled Keywords:||multistated wavelet-based hidden Markov trees; Gaussian mixture model; Kullback–Leibler distance; image retrieval|
|Fields of Research (FOR2008):||08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining|
01 Mathematical Sciences > 0103 Numerical and Computational Mathematics > 010301 Numerical Analysis
08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080106 Image Processing
|Socio-Economic Objective (SEO2008):||E Expanding Knowledge > 97 Expanding Knowledge > 970108 Expanding Knowledge in the Information and Computing Sciences|
|Deposited On:||02 Mar 2012 15:02|
|Last Modified:||23 May 2012 12:49|
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