Information Retrieval
IGroup: presenting web image search results in semantic clusters
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Diversifying image search with user generated content
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
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
Designing Novel Image Search Interfaces by Understanding Unique Characteristics and Usage
INTERACT '09 Proceedings of the 12th IFIP TC 13 International Conference on Human-Computer Interaction: Part II
Multimodal image retrieval over a large database
CLEF'09 Proceedings of the 10th international conference on Cross-language evaluation forum: multimedia experiments
Image ranking based on user browsing behavior
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
CIDER: Concept-based image diversification, exploration, and retrieval
Information Processing and Management: an International Journal
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Even though Web image search queries are often ambiguous, traditional search engines retrieve and present results solely based on relevance ranking, where only the most common and popular interpretations of the query are considered. Rather than assuming that all users are interested in the most common meaning of the query, a more sensible approach may be to produce a diversified set of images that cover the various aspects of the query, under the expectation that at least one of these interpretations will match the searcher's needs. However, such a promotion of diversity in the search results has the side-effect of decreasing the precision of the most common sense. In this paper, we evaluate this trade-off in the context of a method for explicitly diversifying image search results via concept-based query expansion using Wikipedia. Experiments with controlling the degree of diversification illustrate this balance between diversity and precision for both ambiguous and specific queries. Our ultimate goal of this research is to propose an automatic method for tuning the diversification parameter based on degree of ambiguity of the original query.