Classification of Occluded Images for Large-Scale Datasets With Numerous Occlusion Patterns

Large-scale image datasets with numerous occlusion patterns prevail in real applications. The classification scheme based on subspace decomposition-based estimation with squared
$l_{2}$
-norm regularization (SDBE_L2) has shown promising performance for the classification of partially occluded images. For the large-scale image datasets with numerous occlusion patterns, it however suffers from a high labor intensity in acquiring extra image pairs and a large consumption of computational resources in the training stage. To reduce the labor intensity, this paper enumerates several useful types of extra image pairs to guide the collection of extra images and introduces an intra-class random pairing method to semi-automatically form the extra image pairs. To alleviate the consumption of computational resources, this paper proposes two dictionary compression approaches: 1) uncentered PCA-based single partition compression (UPSPC), which compresses the dictionary to a size not larger than twice the column vector length without affecting the classification accuracy, and 2) uncentered PCA-based intra-class partition compression (UPIPC), which can further shrink the occlusion error dictionary (or class dictionary) when it has a small number of occlusion classes (or image classes). The proposed approaches are based on the property of SDBE_L2 being invariant to the uncentered PCA of sub-dictionaries. The extensive experiments on the Caltech-101 dataset and Oxford-102 flower dataset demonstrate the enumerated examples and the intra-class random pairing method facilitate acquiring the extra images and forming the extra image pairs only with a small loss in the classification accuracy. The experimental results on a large-scale occluded image dataset synthesized from the ILSVRC 2012 classification dataset with numerous occlusion patterns show that the proposed dictionary compression approaches reduce the dictionary size by over 11 times and shorten the training time by more than 39 times without loss in the classification accuracy.