Image classification by semisupervised sparse coding with confident unlabeled samples

Sparse coding has achieved very excellent performance in image classification tasks, especially when the supervision information is incorporated into the dictionary learning process. However, there is a large amount of unlabeled samples that are expensive and boring to annotate. We propose an image classification algorithm by semisupervised sparse coding with confident unlabeled samples. In order to make the learnt sparse coding more discriminative, we select and annotate some confident unlabeled samples. A minimization model is developed in which the reconstruction error of the labeled, the selected unlabeled and the remaining unlabeled data and the classification error are integrated, which enhances the discriminant property of the dictionary and sparse representations. The experimental results on image classification tasks demonstrate that our algorithm can significantly improve the image classification performance. (C) 2017 SPIE and IS&T