Non-parametric human segmentation using support vector machine

The human category is among the most commonly captured and therefore important subject in digital cameras. As a result, human segmentation also plays an important role in the camera system. This paper presents a framework of non-parametric learning that achieves human segmentation using support vector machine (SVM). In the training stage, the training human windows are warped to a normalized size and oversegmented into regular superpixels. A two-dimensional (2D) array of SVM models is then trained by extracting various edge and color features from each superpixel. In this process of SVM training, the 2D array of SVM models automatically and effectively learns the characteristics of the human shape. Given an affinely warped human window for testing, the proposed method calculates superpixels' initial scores of belonging to a human (foreground) using the trained SVM models. Finally, the initial prediction scores are effectively propagated by optimizing a well-defined energy function using an estimated confidence map. In experiments on a publicly available challenging dataset, the proposed framework rapidly yields excellent results in human segmentation both qualitatively and quantitatively1.