Query-expanded collaborative representation based classification with class-specific prototypes for object recognition

Linear representation based classifiers (LinearRCs) assume that a query image can be represented as a linear combination of dictionary atoms or prototypes with various priors (e.g., sparsity), which have achieved impressive results in face recognition. Recently, a few attempts have been made to deal with more general cases (e.g., multi-view or multi-pose objects, more generic objects, etc.) but with additional requirements. In this paper, we present a query-expanded collaborative representation based classifier with class-specific prototypes (QCRC_CP) from the general perspective. First, we expand a single query in a multi-resolution way to cover rich variations of object appearances, thereby generating a query set. We then condense the gallery images to a small amount of prototypical images by maximizing canonical correlation in a class-specific way, in which the implicit query-dependent data locality discards the outliers. Given the query set, we finally propose a multivariate LinearRC with collaborative prior to identify the query according to the rule of minimum normalized residual (MNR). Experiments on four object recognition datasets (FERET pose, Swedish leaf, Chars74K, and ETH-80) show that our method outperforms the state-of-the-art LinearRCs with performance increases at least 3.1%, 3.8%, 10.4% and 3.1% compared to other classifiers.