A three-level clustering algorithm for color texture segmentation
Proceedings of the International Conference and Workshop on Emerging Trends in Technology
A fast and robust image segmentation using FCM with spatial information
Digital Signal Processing
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Effective level set image segmentation with a kernel induced data term
IEEE Transactions on Image Processing
A label field fusion Bayesian model and its penalized maximum rand estimator for image segmentation
IEEE Transactions on Image Processing
A de-texturing and spatially constrained K-means approach for image segmentation
Pattern Recognition Letters
Image-to-MIDI mapping based on dynamic fuzzy color segmentation for visually impaired people
Pattern Recognition Letters
MDS-based segmentation model for the fusion of contour and texture cues in natural images
Computer Vision and Image Understanding
Multiscale roughness measure for color image segmentation
Information Sciences: an International Journal
Fast image segmentation based on K-means algorithm
Proceedings of the 4th International Conference on Internet Multimedia Computing and Service
A self-adaptive segmentation method by fusion of multi-color space components
AICI'12 Proceedings of the 4th international conference on Artificial Intelligence and Computational Intelligence
Real web community based automatic image annotation
Computers and Electrical Engineering
Model probability in self-organising maps
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advences in computational intelligence - Volume Part II
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This paper presents a new, simple, and efficient segmentation approach, based on a fusion procedure which aims at combining several segmentation maps associated to simpler partition models in order to finally get a more reliable and accurate segmentation result. The different label fields to be fused in our application are given by the same and simple (K-means based) clustering technique on an input image expressed in different color spaces. Our fusion strategy aims at combining these segmentation maps with a final clustering procedure using as input features, the local histogram of the class labels, previously estimated and associated to each site and for all these initial partitions. This fusion framework remains simple to implement, fast, general enough to be applied to various computer vision applications (e.g., motion detection and segmentation), and has been successfully applied on the Berkeley image database. The experiments herein reported in this paper illustrate the potential of this approach compared to the state-of-the-art segmentation methods recently proposed in the literature.