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
Computer Vision: A Modern Approach
Computer Vision: A Modern Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
A fast watershed algorithm based on chain code and its application in image segmentation
Pattern Recognition Letters
Color image segmentation by analysis of subset connectedness and color homogeneity properties
Computer Vision and Image Understanding
Automatic seeded region growing for color image segmentation
Image and Vision Computing
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The paper presents a modification of a bottom up graph theoretic image segmentation algorithm to improve its performance. This algorithm uses Kruskal's algorithm to build minimum spanning trees for segmentation that reflect global properties of the image: a predicate is defined for measuring the evidence of a boundary between two regions and the algorithm makes greedy decisions to produce the final segmentation. We modify the algorithm by reducing the number of edges required for sorting based on two criteria. We also show that the algorithm produces an over segmented result and suggest a statistical region merge process that will reduce the over segmentation. We have evaluated the algorithm by segmenting various video clips Our experimental results indicate the improved performance and quality of segmentation.