Iterative Semi-Supervised Sparse Coding Model for Image Classification

The scarcity of labeled data and the high-dimensionality of multimedia data are the major obstacles for image classification. Due to these concerns, this paper proposes a novel algorithm, Iterative Semi-supervised Sparse Coding (ISSC), which jointly explores the advantages of both sparse coding and graph-based semi-supervised learning in order to learn discriminative sparse codes as well as an effective classification function. The ISSC algorithm fully exploits initial labels and the subsequently predicted labels for sparse codes learning. At the same time, during the graph-based semi-supervised learning stage, similarity matrix is firstly adjusted through the latest learned sparse codes, and then is utilized to obtain a better classification function. To make the ISSC scale up to larger databases, a novel online dictionary learning algorithm is also proposed to update the dictionary incrementally. In particular, by solving quadratic optimization, the ISSC approach can give rise to closed-form solutions for sparse codes and classification function, respectively. It has been extensively evaluated over three widely used datasets for image classification task. The experimental results in terms of classification accuracy demonstrate the proposed ISSC approach can achieve significant performance improvements with respect to the state-of-the-arts.