A stochastic gravitational approach to feature based color image segmentation
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- 作者：Esmat Rashedi ; Hossein Nezamabadi-pour ; nezam@mail.uk.ac.ir
- 关键词：Color image segmentation ; Feature space ; Gravitational clustering ; Gravitational search algorithm ; Escape velocity
- 刊名：Engineering Applications of Artificial Intelligence
- 出版年：2013
- 出版时间：April, 2013
- 年：2013
- 卷：26
- 期：4
- 页码：1322-1332
- 全文大小：1668 K
文摘
In this paper, a novel image segmentation algorithm based on the theory of gravity is presented, which is called as ¡°stochastic feature based gravitational image segmentation algorithm (SGISA)¡±. The proposed SGISA uses color, texture, and spatial information to partition the image into homogenous and semi-compact segments. The proposed method benefits from the advantages of both clustering and region growing image segmentation techniques. The SGISA is equipped with a new operator called ¡°escape¡± that is inspired by the concept of escape velocity in physics. Moreover, motivated by heuristic search algorithms, we incorporate a stochastic characteristic with the SGISA, which gives algorithm the ability to search the image for finding the fittest regions (pixels) that are suitable for merging. Several experiments on various standard images as well as Berkley standard image database are reported. Results are compared with a well-known clustering based segmentation method, C-means, a gravitational based clustering method (SGC), and the well-known mean-shift method. The results are reported using unsupervised criteria and pre-ground-truthed measures. The obtained results confirm the effectiveness of the proposed method in color image segmentation.