Huimin Xiao

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—A new approach for image segmentation based on visual attention mechanism is proposed. Motivated biologically, this approach simulates the bottom-up human visual selective attention mechanism, extracts early vision features of the image and constructs the saliency map. Multiple image features such as intensity, color and orientation in multiple scales are(More)
—Salient region detection in images is very useful for image processing applications like image compressing, image segmentation, object detection and recognition. In this paper, an improved approach to detect salient region is presented. The proposed method can generate a robust saliency map and extract salient regions with precise boundaries. In the(More)
—Computational model of visual attention has got more and more attention in machine vision and image processing. A hierarchical computational model for selective visual attention is proposed in this paper. This model simulates the attention mechanism from far (coarse) to near (fine) of human visual system. Firstly, the input image is analyzed at the(More)
Twin support vector machine with two nonparallel classifying hyperplanes and its extensions have attracted much attention in machine learning and data mining. However, the prediction accuracy may be highly influenced when noise is involved. In particular, for the least squares case, the intractable computational burden may be incurred for large scale data.(More)
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