Combined local color and texture analysis of stained cells
Computer Vision, Graphics, and Image Processing
A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Structure Representation and Processing: A Discussion of Some Segmentation Methods in Cytology
IEEE Transactions on Pattern Analysis and Machine Intelligence
Floating search methods in feature selection
Pattern Recognition Letters
Texture Features for Browsing and Retrieval of Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Filtering for Texture Classification: A Comparative Study
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Segmentation of Color-Texture Regions in Images and Video
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust mixture modelling using the t distribution
Statistics and Computing
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
WACV-MOTION '05 Proceedings of the Seventh IEEE Workshops on Application of Computer Vision (WACV/MOTION'05) - Volume 1 - Volume 01
Color texture measurement and segmentation
Signal Processing - Special section on content-based image and video retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Graph Cuts and Efficient N-D Image Segmentation
International Journal of Computer Vision
Toward Objective Evaluation of Image Segmentation Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Quaternion color texture segmentation
Computer Vision and Image Understanding
k-means++: the advantages of careful seeding
SODA '07 Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms
Unsupervised segmentation of natural images via lossy data compression
Computer Vision and Image Understanding
Color-texture segmentation using unsupervised graph cuts
Pattern Recognition
Color texture segmentation based on the modal energy of deformable surfaces
IEEE Transactions on Image Processing
Automatic image segmentation by dynamic region growth and multiresolution merging
IEEE Transactions on Image Processing
Image segmentation based on GrabCut framework integrating multiscale nonlinear structure tensor
IEEE Transactions on Image Processing
Colour, texture, and motion in level set based segmentation and tracking
Image and Vision Computing
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
A label field fusion Bayesian model and its penalized maximum rand estimator for image segmentation
IEEE Transactions on Image Processing
Auto-Context and Its Application to High-Level Vision Tasks and 3D Brain Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image segmentation by MAP-ML estimations
IEEE Transactions on Image Processing
Describing Reflectances for Color Segmentation Robust to Shadows, Highlights, and Textures
IEEE Transactions on Pattern Analysis and Machine Intelligence
Color texture analysis based on fractal descriptors
Pattern Recognition
Scene analysis by integrating primitive segmentation andassociative memory
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Information combination operators for data fusion: a comparative review with classification
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Exact optimization for Markov random fields with convex priors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic watershed segmentation of randomly textured color images
IEEE Transactions on Image Processing
Efficient and reliable schemes for nonlinear diffusion filtering
IEEE Transactions on Image Processing
CTex—An Adaptive Unsupervised Segmentation Algorithm Based on Color-Texture Coherence
IEEE Transactions on Image Processing
MDS-Based Multiresolution Nonlinear Dimensionality Reduction Model for Color Image Segmentation
IEEE Transactions on Neural Networks
IEEE Transactions on Image Processing
Hi-index | 0.01 |
This paper proposes an unsupervised color-texture image segmentation method. In order to enhance the effects of segmentation, a new color-texture descriptor is designed by integrating the compact multi-scale structure tensor (MSST), total variation (TV) flow, and the color information. Due to the fact that MSST does not work well for separating regions with large-scale texture, the total variation flow is used to auxiliarily describe the texture feature by extracting local scale information. To segment the color-texture image in an unsupervised and multi-label way, the multivariate mixed student's t-distribution (MMST) is chosen for probability distribution modeling, as MMST can describe the distribution of color-texture features accurately. Since the valid class number is hard to adaptively determine in advance, a component-wise expectation-maximization for MMST (CEM^3ST) algorithm is proposed, which can effectively initialize the valid class number. Then, we can build up the energy functional according to the valid class number, and optimize it by multilayer graph cuts method. However, the problem of over/error-segmentation often happens. To overcome this problem, a strategy of regional credibility merging (RCM) is presented by integrating the regional adjacency relationship, region size, common edge between regions, and regional color-texture dissimilarity. In order to terminate the whole segmentation process, an adaptive iteration convergence criterion is designed, which combines the negative logarithm of probability of all color-texture features with the Kullback-Leibler (KL) divergence for MMST. Experiments using a large number of synthesis color-texture images and real natural scene images demonstrate the superiority of our proposed method, such as the effective over/error-segmentation reduction, high segmentation accuracy, and outperforming visual entirety/consistency.