Contourlet Based Multiresolution Texture Segmentation Using Contextual Hidden Markov Models

• Published in: Intelligent Information Technology pp. 336 - 343
• Main Authors: Raghavendra, B. S., Bhat, P. Subbanna
• Format: Book Chapter Conference Proceeding
• Language: English
• Published: Berlin, Heidelberg Springer Berlin Heidelberg 01.01.2004; Springer
• Series: Lecture Notes in Computer Science
• Subjects: Applied sciences; Artificial intelligence; Computer science; control theory; systems; Exact sciences and technology; Gaussian Mixture Model; Hide Markov Model; Hide State; Learning and adaptive systems; Small Block Size; Texture Segmentation; Statistical analysis; Probabilistic approach; Segmentation; Image databank; Markov model; Modeling; Texture; Competitive algorithms; Multiresolution analysis; Algorithm performance; Wavelet transformation; Hidden Markov model; Maximum likelihood; Artificial intelligence
• ISBN: 9783540241263; 3540241264
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-540-30561-3_35

In this paper, block based texture segmentation is proposed based on contourlets and the hidden Markov model (HMM). Hidden Markov model is combined with hidden Markov tree (HMT) to form HMM-HMT model that models global dependency between the blocks in addition to the local statistics within a block. The HMM-HMT model is modified to use the contourlet transform, a new extension to the wavelet transform that forms a true basis for image representations. The maximum likelihood multiresolution segmentation algorithm is used to handle several block sizes at once. Since the algorithm works on the contourlet transformed image data, it can directly segment images without the need for transforming into the space domain. The experimental results demonstrate the competitive performance of the algorithm on contourlets with that of the other methods and excellent visual performance at small block sizes.The performance is comparable with that of wavelets and is superior at small block sizes.