Self-paced dictionary learning for image classification

Image classification is an important research task in multimedia content analysis and processing. Learning a compact dictionary easying to derive sparse representation is one of the focused issues in the state-of-the-art image classification framework. Most existing dictionary learning approaches assign equal importance to all training samples, which in fact have different complexity in terms of sparse representation. Meanwhile, the contextual information "hidden" in different samples is ignored as well. In this paper, we propose a self-paced dictionary learning algorithm in order to accommodate the "hidden" information of the samples into the learning procedure, which uses the easy samples to train the dictionary first, and then iteratively introduces more complex samples in the remaining training procedure until the entire training data are all easy samples. The algorithm adaptively chooses the easy samples in each iteration, while the learned dictionary in the previous iteration is in turn used as a basis for the current iteration. This strategy implicitly takes advantage of the contextual relationships among training samples. The number of the chosen samples in each iteration is determined by an adaptive threshold function proposed in this paper. Experimental results on benchmark datasets, including Caltech-101 and 15-Scene, show that our algorithm leads to better dictionary representation and classification performance than the baseline methods.