  For an object classification system, the most critical obstacles towards
real-world applications are often caused by large intra-class variability,
arising from different lightings, occlusion and corruption, in limited sample
sets. Most methods in the literature would fail when the training samples are
heavily occluded, corrupted or have significant illumination or viewpoint
variations. Besides, most of the existing methods and especially deep
learning-based methods, need large training sets to achieve a satisfactory
recognition performance. Although using the pre-trained network on a generic
large-scale dataset and fine-tune it to the small-sized target dataset is a
widely used technique, this would not help when the content of base and target
datasets are very different. To address these issues, we propose a joint
projection and low-rank dictionary learning method using dual graph constraints
(JP-LRDL). The proposed joint learning method would enable us to learn the
features on top of which dictionaries can be better learned, from the data with
large intra-class variability. Specifically, a structured class-specific
dictionary is learned and the discrimination is further improved by imposing a
graph constraint on the coding coefficients, that maximizes the intra-class
compactness and inter-class separability. We also enforce low-rank and
structural incoherence constraints on sub-dictionaries to make them more
compact and robust to variations and outliers and reduce the redundancy among
them, respectively. To preserve the intrinsic structure of data and penalize
unfavourable relationship among training samples simultaneously, we introduce a
projection graph into the framework, which significantly enhances the
discriminative ability of the projection matrix and makes the method robust to
small-sized and high-dimensional datasets.
