Physics-based segmentation of complex objects using multiple hypotheses of image formation
Computer Vision and Image Understanding - Special issue on physics-based modeling and reasoning in computer vision
Normalized Cuts and Image Segmentation
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
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Yet Another Survey on Image Segmentation: Region and Boundary Information Integration
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Segmentation Using Eigenvectors: A Unifying View
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
A Database of Human Segmented Natural Images and its Application to
A Database of Human Segmented Natural Images and its Application to
Multiclass Spectral Clustering
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
Spectral Segmentation with Multiscale Graph Decomposition
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Toward Objective Evaluation of Image Segmentation Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Color Image Segmentation Based on Mean Shift and Normalized Cuts
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Rapid image segmentation using color, texture and syntactic visual features
AICI'12 Proceedings of the 4th international conference on Artificial Intelligence and Computational Intelligence
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In this paper we propose an hybrid segmentation algorithm which incorporates the advantages of the efficient graph based segmentation and normalized cuts partitioning algorithm. The proposed method requires low computational complexity and is therefore suitable for realtime image segmentation processing. Moreover, it provides effective and robust segmentation. For that, our method consists first, at segmenting the input image by the "Efficient Graph-Based" segmentation. The segmented regions are then represented by a graph structure. As a final step, the normalized cuts partitioning algorithm is applied to the resulting graph in order to remove non-significant regions. In the proposed method, the main computational cost is the efficient graph based segmentation cost since the computational cost of partitioning regions using the Ncut method is negligibly small. The efficiency of the proposed method is demonstrated through a large number of experiments using different natural scene images.