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With the purpose of designing a general learning framework for detecting human parts, we formulate this task as a classification problem over non-aligned training examples of multiple classes. We propose a new multi-class multiinstance boosting method, named MCMIBoost, for effective human parts detection in static images. MCMIBoost has two benefits. First, training examples are represented as a set of non-aligned instances, so that the alignment problem caused by human appearance variation candoi:10.1109/iccvw.2009.5457475 dblp:conf/iccvw/ChenCHC09 fatcat:skuhg5vpjjgv5ehjngyqpv3cvq