A customized Gabor filter for unsupervised color image segmentation
Image and Vision Computing
Improved Adaptive Spatial Information Clustering for Image Segmentation
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing
Adaptive spatial information-theoretic clustering for image segmentation
Pattern Recognition
Region-based Deformable Net for automatic color image segmentation
Image and Vision Computing
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Image segmentation using normalized cuts and efficient graph-based segmentation
ICIAP'11 Proceedings of the 16th international conference on Image analysis and processing - Volume Part II
GeneSIS: A GA-based fuzzy segmentation algorithm for remote sensing images
Knowledge-Based Systems
Hi-index | 0.00 |
This paper proposes a methodology that incorporates principles from cluster analysis and graph representation to achieve efficient image segmentation results. More specifically, a feature-based, inter-region dissimilarity relation is considered here in order to determine the dissimilarity matrix in a graph-based segmentation scheme. The calculation of the dissimilarity function between adjacent elementary image regions is based on the proximity of each region's feature vector to the main clusters that are formed by the image samples in the feature space. In contrast to typical segmentation approaches of the literature, the global feature space information is included in the spatial graph representation that was derived from the initial Watershed partitioning. A region grouping process is applied next to form the final segmentation results. The proposed approach was also compared to approaches that use feature-based, or spatial information exclusively, to indicate its effectiveness.