Vector quantization and signal compression
Vector quantization and signal compression
Sparse Approximate Solutions to Linear Systems
SIAM Journal on Computing
Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
International Journal of Computer Vision
Video Google: A Text Retrieval Approach to Object Matching in Videos
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
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
A Bayesian Hierarchical Model for Learning Natural Scene Categories
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - 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
Method of optimal directions for frame design
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 05
Canonical image selection from the web
Proceedings of the 6th ACM international conference on Image and video retrieval
Robust Face Recognition via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Online dictionary learning for sparse coding
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
JustClick: personalized image recommendation via exploratory search from large-scale Flickr images
IEEE Transactions on Circuits and Systems for Video Technology
NUS-WIDE: a real-world web image database from National University of Singapore
Proceedings of the ACM International Conference on Image and Video Retrieval
Exploiting Textons distributions on spatial hierarchy for scene classification
Journal on Image and Video Processing - Special issue on selected papers from multimedia modeling conference 2009
Summarization of archived and shared personal photo collections
Proceedings of the 20th international conference companion on World wide web
Effective summarization of large-scale web images
MM '11 Proceedings of the 19th ACM international conference on Multimedia
-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation
IEEE Transactions on Signal Processing
Matching pursuits with time-frequency dictionaries
IEEE Transactions on Signal Processing
Video Précis: Highlighting Diverse Aspects of Videos
IEEE Transactions on Multimedia
Image quality assessment: from error visibility to structural similarity
IEEE Transactions on Image Processing
Towards Scalable Summarization of Consumer Videos Via Sparse Dictionary Selection
IEEE Transactions on Multimedia
Learning group-based dictionaries for discriminative image representation
Pattern Recognition
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In this paper, a novel approach is developed to achieve automatic image collection summarization. The effectiveness of the summary is reflected by its ability to reconstruct the original set or each individual image in the set. We have leveraged the dictionary learning for sparse representation model to construct the summary and to represent the image. Specifically we reformulate the summarization problem into a dictionary learning problem by selecting bases which can be sparsely combined to represent the original image and achieve a minimum global reconstruction error, such as MSE (Mean Square Error). The resulting ''Sparse Least Square'' problem is NP-hard, thus a simulated annealing algorithm is adopted to learn such dictionary, or image summary, by minimizing the proposed optimization function. A quantitative measurement is defined for assessing the quality of the image summary by investigating both its reconstruction ability and its representativeness of the original image set in large size. We have also compared the performance of our image summarization approach with that of six other baseline summarization tools on multiple image sets (ImageNet, NUS-WIDE-SCENE and Event image set). Our experimental results have shown that the proposed dictionary learning approach can obtain more accurate results as compared with other six baseline summarization algorithms.