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This paper presents an algorithm for object localization and segmentation. The algorithm uses machine learning, and statistical and combinatorial optimization tools to build a tracker that is robust to noise and occlusions. The method is based on a novel energy formulation and its dual use for object localization and segmentation. The energy uses kernel principal component analysis to incorporate shape and appearance constraints of the target object and the background. The energy arising from the procedure is equivalent to an un-normalized density function, thus providing a probabilistic interpretation to the procedure. Mean-shift optimization finds the most probable location of the target object. Graph-cut maximization on the localized object window in the image generates the globally optimal segmentation.