Robust single-object image segmentation based on salient transition region

Existing transition region-based image thresholding methods are unstable, and fail to achieve satisfactory segmentation accuracy on images with overlapping gray levels between object and background. This is because they only take the gray level mean of pixels in transition regions as the segmentation threshold of the whole image. To alleviate this issue, we proposed a robust hybrid single-object image segmentation method by exploiting salient transition region. Specifically, the proposed method first uses local complexity and local variance to identify transition regions of an image. Secondly, the transition region with the largest pixel number is chosen as salient transition region. Thirdly, a gray level interval is determined by using transition regions and image information, and one gray level of the interval is determined as the segmentation threshold by using the salient transition region. Finally, the image thresholding result is refined as final segmentation result by using the salient transition region to remove fake object regions. The proposed method has been extensively evaluated by experiments on 170 single-object real world images. Experimental results show that the proposed method achieves better segmentation accuracy and robustness than several types of image segmentation techniques, and enjoys its nature of simplicity and efficiency.