Hybrid Framework to Image Segmentation

Authors:
Fernando C. Monteiro
Affiliations:
Polytechnic Institute of Bragança, Portugal
Venue:
ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part II
Year:
2009

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Abstract

This paper proposes a new hybrid framework to image segmentation which combines edge- and region-based information with spectral techniques through the morphological algorithm of watersheds. The image is represented by a weighted undirected graph, whose nodes correspond to over-segmented regions (atomic regions), instead of pixels, that decreases the complexity of the overall algorithm. In addition, the link weights between the nodes are calculated through the intensity similarities combined with the intervening contours information among atomic regions. We outline a procedure for algorithm evaluation through the comparison with some of the most popular segmentation algorithms: the mean-shift-based algorithm, a multiscale graph based segmentation method, and JSEG method for multiscale segmentation of colour and texture. Experiments on the Berkeley segmentation database indicate that the proposed segmentation framework yields better segmentation results due to its region-based representation.