Semi-Supervised Bi-Dictionary Learning for Image Classification With Smooth Representation-Based Label Propagation

In this paper, we propose semi-supervised bi-dictionary learning for image classification with smooth representation-based label propagation (SRLP). Natural images contain complex contents of multiple objects with complicated background, clutter, and occlusions, which prevents image features from belonging to a specific category. Therefore, we employ reconstruction-based classification to implement discriminative dictionary learning in a probabilistic manner. We jointly learn a discriminative dictionary called anchor in the feature space and its corresponding soft label called anchor label in the label space, where the combination of anchor and anchor label is referred to as bi-dictionary. The learnt bi-dictionary is utilized to bridge the semantic gap in image classification. First, SRLP constructs smoothed reconstruction problems for bi-dictionary learning. Then, SRLP produces the reconstruction coefficients in the feature space over the anchor to infer soft labels of samples in the label space. Experimental results demonstrate that the proposed method is capable of learning a pair of discriminative dictionaries for image classification in the feature and label spaces and outperforms the-state-of-the-art reconstruction-based classification ones.