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In this paper, we study the salient object detection problem for images. We formulate this problem as a binary labeling task where we separate the salient object from the background. We propose a set of novel features, including multiscale contrast, center-surround histogram, and color spatial distribution, to describe a salient object locally, regionally, and globally. A conditional random field...
For the purpose of color image segmentation, an unsupervised peak value searching algorithm was proposed, which was used to determine the approximate dominant color components of image. First, the local peaks of 3D color histogram within the neighborhood of 3 times 3 times 3 were located. The corresponding color values of local peaks were regarded as initial clustering centers, and the number of local...
We propose a new algorithm, the multi-view expectation and maximization algorithm (multi-view EM), to deal with real-world learning problems where there are some natural split of features. Multi-view EM does feature split in the same manner as co-training and co-EM, two successful semi-supervised learning algorithms in text learning tasks, but it considers the multi-view learning problem in the framework...
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