Sparse Approximate Solutions to Linear Systems
SIAM Journal on Computing
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Selecting Canonical Views for View-Based 3-D Object Recognition
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Generating summaries for large collections of geo-referenced photographs
Proceedings of the 15th international conference on World Wide Web
Video summarization by k-medoid clustering
Proceedings of the 2006 ACM symposium on Applied computing
Canonical image selection from the web
Proceedings of the 6th ACM international conference on Image and video retrieval
Image collection summarization via dictionary learning for sparse representation
Pattern Recognition
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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.