| Summary: | 碩士 === 中原大學 === 工業工程研究所 === 92 === Image segmentation technology evolution has a trend toward the three-dimensional color image instead of the one-dimensional gray image. Cluster-based image segmentation algorithms take advantage of color image with more data in three-dimension. However, most cluster-based color image segmentation algorithms discussed in the literature only consider the distribution of a color space, but they seldom consider the features of the image domain. This could result in over-segmentation. Due to this concern this study proposes a two-phase method for the segmentation of color images. In the fist phase, we use the self-organizing feature map (SOM) network method to reduce the information to represent an image by neurons or clustering centers. In the second phase, we merge regions of the segmented image by the region features including color, position, and shape. This research proposes the method to find the suitable number of regions after segmentation; more regions result in over-segmentation, but less regions may distort the original image. This study uses three quantitative evaluation functions of color image segmentation to compare our method with three other methods: the improved single stage SOM method, our method but only color feature is considered, and the two-stage heuristic SOM method, and it is proved that the proposed method has the best performance.
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