  Image classification is a challenging problem for computer in reality. Large
numbers of methods can achieve satisfying performances with sufficient labeled
images. However, labeled images are still highly limited for certain image
classification tasks. Instead, lots of unlabeled images are available and easy
to be obtained. Therefore, making full use of the available unlabeled data can
be a potential way to further improve the performance of current image
classification methods. In this paper, we propose a discriminative
semi-supervised sparse representation algorithm for image classification. In
the algorithm, the classification process is combined with the sparse coding to
learn a data-driven linear classifier. To obtain discriminative predictions,
the predicted labels are regularized with three graphs, i.e., the global
manifold structure graph, the within-class graph and the between-classes graph.
The constructed graphs are able to extract structure information included in
both the labeled and unlabeled data. Moreover, the proposed method is extended
to a kernel version for dealing with data that cannot be linearly classified.
Accordingly, efficient algorithms are developed to solve the corresponding
optimization problems. Experimental results on several challenging databases
demonstrate that the proposed algorithm achieves excellent performances
compared with related popular methods.
