FeaNorm is an intuitive study to compare different feature normalization strategies for part-based image classification. The part-based models used in this paper are Spatial Pyramid Matching (SPM) [Lazebnik, CVPR06] and Hierarchical Part Matching (HPM) [Xie, ICCV13]. In the context of SVM classifier, we first formulate power and coeffient, two key parameters in feature normalization, and then propose two part-based properties, i.e., the independent assumption and the hierarchical-contribution assumption. In this way, it is possible to generate more discriminative image representation with merely no extra computational overheads. The contribution of this paper is twofold:
- We verify that L1-norm does NOT result in much lower classification accuracy than L2-norm, which is a popular opinion [Boureau, CVPR10] [Wang, CVPR10].
- We propose to use part-based priors for a more intuitive and balanced normalization strategy, which does produce better classification results.