Feature normalization for part-based image classification
Part-based Bag-of-Features (BoF) models such as Spatial Pyramid Matching (SPM) play an important role in image classification. Before sending the feature vectors into classifiers for training and testing, it is required to normalize them in order to approximately equalize ranges of the attributes and make them have comparable effects in distance computation. Although some works have been focused on general feature normalization, we do not see any discussion on specialized normalization algorithms for part-based BoF models. In this paper, we fill in the blank with extensive experiments and discussions. Based on solid normalization parameters (power and coefficient), we further study two straightforward part-based properties, i.e., the independent assumption and the hierarchical-contribution assumption, to scale the feature super-vectors separately. Finally, we test our algorithm on challenging image sets, i.e., Caltech 101 and CUB-200-2011, for general and fine-grained classification, and show its efficiency, scalability and adaptability in both scenarios.