Abstract: A salient object segmentation method based on low-level visual feature and middle-level visual cues was proposed. First, the low-level visual feature of the original image was extract via color, intensity, orientation and local energy feature channels to build the saliency map. The salient feature mask was acquired via the maximum information entropy principle. Then In middle-level, the visual cues were applied for over-segmentation of an image into superpixels. In clustering, the spatial information of the feature vector was taken into consideration according to the salient intensity, and the initial parameters were automatically set. Thus, the superpixels after segmentation accurately approach the object contour. Finally, for segmenting the irregular object from background, the superpixels were classified using the salient feature mask, and the low-level and middle-level features were fused. The experiment results demonstrate that the proposed method is less sensitive to complex illumination and background, and can be used to segment contour accurately. Moreover, it can be applied to segment irregular objects from complex background.

Key words: computer application, superpixels, image segmentation, visual salient features, saliency map