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Large scale image understanding is drawing more and more attention from the researchers and industry. Inspired by the game theory and machine learning algorithm, this paper proposes a semantic dictionary to solve the key problem of visual polysemia and concept polymorphism in the large scale image understanding. The semantic dictionary characterizes the probability distribution between visual appearances...
Previous Multiple Kernel Learning approaches (MKL) employ different kernels by their linear combination. Though some improvements have been achieved over methods using single kernel, the advantages of employing multiple kernels for machine learning are far from being fully developed. In this paper, we propose to use “high order kernels” to enhance the learning of MKL when a set of original kernels...
Pedestrian detection in still image should handle the large appearance and pose variations arising from the articulated structure and various clothing of human bodies as well as view points. So it is difficult to design effective classifier for this problem. In this paper, we address these variations in detection via multiple instance learning, specifically logistic multiple instance boosting (LMIB)...
Tracking 3D people from monocular video is often poorly constrained. To mitigate this problem, prior knowledge should be exploited. In this paper, the Gaussian process spatio-temporal variable model (GPSTVM), a novel dynamical system modeling method is proposed for learning human pose and motion priors. The GPSTVM provides a low dimensional embedding of human motion data, with a smooth density function...
Shot detection is the first stage of video analysis. In this paper, we present a machine learning based shot detection approach using hidden Markov models (HMMs), in which both the color and shape clues are utilized. Its advantages are twofold. First, the temporal characteristics of different shot transitions are exploited and an HMM is constructed for each type of shot transitions, including cut...
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