Knowledge-based interpretation of outdoor natural color scenes
Knowledge-based interpretation of outdoor natural color scenes
Texture Classification by Wavelet Packet Signatures
IEEE Transactions on Pattern Analysis and Machine Intelligence
Wavelet-based statistical signal processing using hidden Markovmodels
IEEE Transactions on Signal Processing
IEEE Transactions on Multimedia
Multiresolution Gauss-Markov random field models for texture segmentation
IEEE Transactions on Image Processing
Bayesian tree-structured image modeling using wavelet-domain hidden Markov models
IEEE Transactions on Image Processing
Multiscale image segmentation using wavelet-domain hidden Markov models
IEEE Transactions on Image Processing
Wavelet-based texture retrieval using generalized Gaussian density and Kullback-Leibler distance
IEEE Transactions on Image Processing
Wavelet transform and adaptive neuro-fuzzy inference system for color texture classification
Expert Systems with Applications: An International Journal
Multiscale fusion of wavelet-domain hidden Markov tree through graph cut
Image and Vision Computing
Multiscale information fusion by graph cut through convex optimization
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part III
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The wavelet-domain hidden Markov model (WD HMM), in particular the hidden Markov tree (HMT), has recently been proposed and applied to gray texture analysis with encouraging results. For color texture analysis, the previous WD HMM can only be used to model the different color planes individually, assuming they are independent of each other. However, this assumption in general is unrealistic. We show in this paper that the wavelet coefficients have certain inter-dependences between color planes. This paper presents a novel approach to modeling the dependences between color planes as well as the interactions across scales. In our approach, the wavelet coefficients at the same location, scale and sub-band, but different color planes are grouped into one vector. We then propose a multivariate Gaussian mixture model (MGMM) for approximating the marginal distribution of the wavelet coefficient vectors in one scale and capturing the interactions of different color planes. In addition, the statistical dependence between different scales is captured by the transition matrix of the hidden Markov tree. Using this approach, we can improve the performance of the WD HMM on the color texture classification. The experiments show that our WD HMM approach provides 85% of correct classifications (PCC) on 68 color textures from Oulu texture database and outperforms the other wavelet-based methods we study. In this paper, we also investigated the classification performance of the WD HMM methods on different color spaces.