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| Image Automatic Segmentation Based on Fast Online Active Learning |
| YAN Jing, PAN Chen, YIN Haibing |
| College of Information Engineering, China Jiliang University, HangZhou 310018 |
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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.
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Received: 26 October 2015
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About author:: YAN Jing, born in 1991, master student. Her research inte-
rests include machine vision and pattern recognition.PAN Chen(Corresponding author), born in 1966, Ph.D., professor. His research interests include machine vision, pattern recognition and medical image processing.YIN Haibing, born in 1974, Ph.D., professor. His research interests include video coding.) |
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2016, Vol. 29 
