Effective summarization of large-scale web images

In this paper, we present a novel framework to achieve effective summarization of large-scale web images by treating the problem of automatic image summarization as the problem of dictionary learning for sparse coding, e.g., the summary of a given image set can be treated as a sparse representation of the given image set (i.e., sparse dictionary for the given image set). For a given semantic category (i.e., certain object class or image concept), we build a sparsity model to reconstruct all its relevant images by using a subset of most representative images (i.e., image summary); and a stepwise basis selection algorithm is developed to learn such sparse dictionary (i.e., image summary) by minimizing an explicit optimization function. By investigating their reconstruction ability, the reconstruction Mean Square Error (MSE) is adapted to objectively measure the performance of various algorithms for automatic image summarization. Our experimental results demonstrate that our dictionary learning for sparse representation algorithm can obtain more accurate summary as compared with other baseline algorithms for automatic image summarization.