Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Normalized Cuts and Image Segmentation
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
Signal Processing, Image Processing and Pattern Recognition
Signal Processing, Image Processing and Pattern Recognition
Yet Another Survey on Image Segmentation: Region and Boundary Information Integration
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
Comparing clusterings: an axiomatic view
ICML '05 Proceedings of the 22nd international conference on Machine learning
Toward Objective Evaluation of Image Segmentation Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised segmentation of natural images via lossy data compression
Computer Vision and Image Understanding
Color texture segmentation based on the modal energy of deformable surfaces
IEEE Transactions on Image Processing
Segmentation of Multivariate Mixed Data via Lossy Data Coding and Compression
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised multiscale color image segmentation based on MDL principle
IEEE Transactions on Image Processing
Image Denoising by Averaging of Piecewise Constant Simulations of Image Partitions
IEEE Transactions on Image Processing
Segmentation by Fusion of Histogram-Based -Means Clusters in Different Color Spaces
IEEE Transactions on Image Processing
A de-texturing and spatially constrained K-means approach for image segmentation
Pattern Recognition Letters
MDS-based segmentation model for the fusion of contour and texture cues in natural images
Computer Vision and Image Understanding
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In this paper, a new unsupervised hierarchical approach to textured color images segmentation is proposed. To this end, we have designed a two-step procedure based on a grey-scale Markovian over-segmentation step, followed by a Markovian graph-based clustering algorithm, using a decreasing merging threshold schedule, which aims at progressively merging neighboring regions with similar textural features. This Hierarchical segmentation method, using two levels of representation, has been successfully applied on the Berkeley Segmentation Dataset and Benchmark (BSDB[1]). The experiments reported in this paper demonstrate that the proposed method is efficient in terms of visual evaluation and quantitative performance measures and performs well compared to the best existing state-of-the-art segmentation methods recently proposed in the literature.