Rapid image segmentation using color, texture and syntactic visual features
AICI'12 Proceedings of the 4th international conference on Artificial Intelligence and Computational Intelligence
A game-theoretical approach to image segmentation
CVM'12 Proceedings of the First international conference on Computational Visual Media
Break and conquer: efficient correlation clustering for image segmentation
SIMBAD'13 Proceedings of the Second international conference on Similarity-Based Pattern Recognition
Integrating cue descriptors in bubble space for place recognition
ICVS'13 Proceedings of the 9th international conference on Computer Vision Systems
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We present a bottom-up aggregation approach to image segmentation. Beginning with an image, we execute a sequence of steps in which pixels are gradually merged to produce larger and larger regions. In each step, we consider pairs of adjacent regions and provide a probability measure to assess whether or not they should be included in the same segment. Our probabilistic formulation takes into account intensity and texture distributions in a local area around each region. It further incorporates priors based on the geometry of the regions. Finally, posteriors based on intensity and texture cues are combined using “ a mixture of experts” formulation. This probabilistic approach is integrated into a graph coarsening scheme, providing a complete hierarchical segmentation of the image. The algorithm complexity is linear in the number of the image pixels and it requires almost no user-tuned parameters. In addition, we provide a novel evaluation scheme for image segmentation algorithms, attempting to avoid human semantic considerations that are out of scope for segmentation algorithms. Using this novel evaluation scheme, we test our method and provide a comparison to several existing segmentation algorithms.