ICSES Transactions on Image Processing and Pattern Recognition

Vol. 4, No. 4, Dec. 2018

Image Segmentation Based on Graph Theory and Threshold | Book Chapter of Image Segmentation: A Guide to Image Mining

a PRMCEAM, Amravati, India

Highlights and Novelties

1- The contours obtained are pertinent to the true edges of the image and could be shown from the exhaustive experimentation.

2- The algorithm captures perceptually important regions and can be verified by comparing with the ground truth edge detection data.

3- Time taken for graph segmentation required less time for almost all the mentioned images.

4- The proposed approach can work on real-time systems and practical applications.

Manuscript Abstract
This paper presents an image segmentation technique using discreet tools from graph theory. The image segmentation incorporating graph theoretic methods make the formulation of the problem suppler and the computation more ingenious. In our proposed method, the problem is modeled by partitioning a graph into several sub-graphs; in such a way that each of the subgraphs represents an eloquent region of the image. The segmentation is performed in a spatially discrete space by the efficient tools from graph theory. After the brief literature review, we have formulated the problem using graph representation of image and the threshold function. The borders between the different regions in an image are identified as per the segmentation criteria and, later, the partitioned regions are branded with random colors. In our approach, in order to make the segmentation fast, the image is preprocessed by DWT and coherence filter before performing the segmentation. We have carried out the experiments on numerous natural images available from Berkeley Image Database as well as synthetic images taken from online resources. The images are preprocessed using the wavelets of Haar, DB2, DB4, DB6 and DB8. In order to evaluate and compare the results, we have used the performance evaluation parameters like Performance Ratio, execution time, PSNR, Precision and Recall and found that the obtained results are promising.

 Digital Image Segmentation   Graph Theory   Image Processing   Preprocessing   Threshold   Wavelet Transform 

© Copyright was transferred to International Computer Science and Engineering Society (ICSES) by all the Authors.

Cite this manuscript as
Vikramsingh Parihar, "Image Segmentation Based on Graph Theory and Threshold," in Image Segmentation: A Guide to Image Mining, 1st ed., ITIPPR: ICSES, 2018, pp. 61-82.

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