基于超像素多特征融合的快速图像分割算法

为了提高高分辨率图像分割效率，解决复杂图案中待分割目标边缘附近前景与背景区分度小而造成的分割目标不完整问题，本文通过引入超像素HOG特征，提出了一种基于超像素多特征融合（superpixel multi-feature fusion，SMFF）的快速图像分割算法.首先采用目前最有效的超像素算法对待分割图像进行超像素预分割，然后提取基于超像素的HOG特征、Lab颜色特征和空间位置特征，设计基于超像素的多特征度量算法，最终采用图割理论实现了基于超像素多特征融合的快速图像分割.实验结果验证了本文算法的有效性，其算法性能接近于目前最经典图像分割算法，且本文算法的时间性能要明显优于其它对比算法.

Fast Image Segmentation Algorithm Based on Superpixel Multi-feature Fusion

In order to improve the efficiency of high-resolution image segmentation and solve the problem of incomplete segmentation caused by small discrimination of foreground and background in the complex pattern near the edge of the target to be segmented,we propose a fast image segmentation algorithm based on superpixel multi-feature fusion (SMFF).Firstly,the most effective superpixel algorithm is used for superpixel processing,and then the superpixel-based HOG feature,laboratory color feature and spatial position feature are extracted.Lastly,by designing a multi-feature measurement algorithm,the fast image segmentation algorithm based on superpixel multi-feature fusion is implemented.Experimental results verify the effectiveness of the proposed algorithm,which is close to the most classical image segmentation algorithm,and the time performance of the proposed algorithm is significantly better than other comparison algorithms.