Abstract：An algorithm for image segmentation is proposed by building a pixels classification model. The model is trained online fast with a feed-forward neural network. Firstly, saliency map is computed by spectral residual (SR) approach. Then, multi-scale analysis is conducted via dispersion of minority high saliency points, and saliency map and gaze areas highly matching with human visual system are obtained. Next, positive and negative samples are selected randomly from saliency and non-saliency regions to compose the training set. A two-class random weighted feed-forward neural network model is trained. Finally, whole image pixels are classified by this model, and image segmentation is realized. Experiments show that the proposed algorithm enhances the segmentation performance for salient object grounded on the spectral residual based method, and the segmentation results are close to human visual perception.
严静，潘晨，殷海兵. 快速在线主动学习的图像自动分割算法*[J]. 模式识别与人工智能, 2016, 29(9): 816-824.
YAN Jing, PAN Chen, YIN Haibing. Image Automatic Segmentation Based on Fast Online Active Learning. , 2016, 29(9): 816-824.
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2016, Vol. 29 
