Effective Summarization of Large-Scale Web Images

In this paper, we present a novel framework to achieve effec-tive 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 represen-tation 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 reconstruc-tion ability, the reconstruction Mean Square Error (MSE) is adapted to objectively measure the performance of vari-ous algorithms for automatic image summarization. Our ex-perimental results demonstrate that our dictionary learning for sparse representation algorithm can obtain more accu-rate summary as compared with other baseline algorithms for automatic image summarization