Generating summaries for large collections of geo-referenced photographs
Proceedings of the 15th international conference on World Wide Web
Kodak's consumer video benchmark data set: concept definition and annotation
Proceedings of the international workshop on Workshop on multimedia information retrieval
Generating diverse and representative image search results for landmarks
Proceedings of the 17th international conference on World Wide Web
The Generalized Maximum Coverage Problem
Information Processing Letters
Proceedings of the Second ACM International Conference on Web Search and Data Mining
Visual diversification of image search results
Proceedings of the 18th international conference on World wide web
An axiomatic approach for result diversification
Proceedings of the 18th international conference on World wide web
Ranking and classifying attractiveness of photos in folksonomies
Proceedings of the 18th international conference on World wide web
The Facebook Effect: The Inside Story of the Company That Is Connecting the World
The Facebook Effect: The Inside Story of the Company That Is Connecting the World
Image collection summarization via dictionary learning for sparse representation
Pattern Recognition
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The volume of personal photos hosted on photo archives and social sharing platforms has been increasing exponentially. It is difficult to get an overview of a large collection of personal photos without browsing though the entire database manually. In this research, we propose a framework to generate representative subset summaries from photo collections hosted on web archives or social networks. We define salient properties of an effective photo summary and model summarization as an optimization of these properties, given the size constraints. We also introduce metrics for evaluating photo summaries based on their information content and the ability to satisfy user's information needs. Our experiments show that our summarization framework performs better than baseline algorithms.