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Semi-supervised learning of sparse representations to recognize people spatial orientation
Year: 2014  
Authors: Nicoletta Noceti, Francesca Odone  
Book title: International Conference on Image Processing
In this paper we consider the problem of classifying people spatial orientation with respect to the camera viewpoint from 2D images. Structured multi-class feature selection allows us to control the amount of redundancy of our input data, while semi-supervised learning helps us coping with the intrinsic ambiguity of output labels. We model the multi-class classification problem with an all-pairs strategy based on the use of a coding matrix. A thorough experimental evaluation on the TUD Multiview Pedestrian benchmark dataset demonstrates the superiority of our approach w.r.t. state-of-the-art.