Image segmentation is an essential task in computer vision and pattern recognition. There are two key challenges for image\nsegmentation. One is to find the most discriminative image feature set to get high-quality segments. The other is to achieve good\nperformance among various images. In this paper,wefirstlypropose a selective feature fusionalgorithmtochoose thebest feature set\nby evaluating the results of presegmentation. Specifically, the proposed method fuses selected features and applies the fused features\nto region growing segmentation algorithm. To get better segments on different images, we further develop an algorithm to change\nthreshold adaptively for each image by measuring the size of the region. The adaptive threshold can achieve better performance\non each image than fixed threshold. Experimental results demonstrate that our method improves the performance of traditional\nregion growing by selective feature fusion and adaptive threshold.Moreover, our proposed algorithm obtains promising results and\noutperforms some popular approaches.
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