NDLI: Improving Diversity in Image Search via Supervised Relevance Scoring

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Improving Diversity in Image Search via Supervised Relevance Scoring

Content Provider	ACM Digital Library
Author	Kompatsiaris, Yiannis Popescu, Adrian Vlahavas, Ioannis Spyromitros-Xioufis, Eleftherios Papadopoulos, Symeon Ginsca, Alexandru Lucian
Abstract	Results returned by commercial image search engines should include relevant and diversified depictions of queries in order to ensure good coverage of users' information needs. While relevance has drastically improved in recent years, diversity is still an open problem. In this paper we propose a reranking method that could be implemented on top of such engines in order to provide a better balance between relevance and diversity. Our method formulates the reranking problem as an optimization of a utility function that jointly considers relevance and diversity. Our main contribution is the replacement of the unsupervised definition of relevance that is commonly used in this formulation with a supervised classification model that strives to capture a query and application-specific notion of relevance. This model provides more accurate relevance scores that lead to significantly improved diversification performance. Furthermore, we propose a stacking-type ensemble learning approach that allows combining multiple features in a principled way when computing the relevance of an image. An empirical evaluation carried out on the datasets of the MediaEval 2013 and 2014 "Retrieving Diverse Social Images" (RDSI) benchmarks confirms the superior performance of the proposed method compared to other participating systems as well as a state-of-the-art, unsupervised reranking method.
Starting Page	323
Ending Page	330
Page Count	8
File Format	PDF
ISBN	9781450332743
DOI	10.1145/2671188.2749334
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-06-22
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Relevance feedback Image retrieval Diversity Image reranking Image classification
Content Type	Text
Resource Type	Article

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