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Diversifying image search with user generated content

Published: 30 October 2008 Publication History

Abstract

Large-scale image retrieval on the Web relies on the availability of short snippets of text associated with the image. This user-generated content is a primary source of information about the content and context of an image. While traditional information retrieval models focus on finding the most relevant document without consideration for diversity, image search requires results that are both diverse and relevant. This is problematic for images because they are represented very sparsely by text, and as with all user-generated content the text for a given image can be extremely noisy.
The contribution of this paper is twofold. First, we present a retrieval model which provides diverse results as a property of the model itself, rather than in a post-retrieval step. Relevance models offer a unified framework to afford the greatest diversity without harming precision. Second, we show that it is possible to minimize the trade-offs between precision and diversity, and estimating the query model from the distribution of tags favors the dominant sense of a query. Relevance models operating only on tags offers the highest level of diversity with no significant decrease in precision.

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  • (2022)An overview of cluster-based image search result organization: background, techniques, and ongoing challengesKnowledge and Information Systems10.1007/s10115-021-01650-9Online publication date: 11-Feb-2022
  • (2020)Heterogeneous-Graph-Based Video Search Reranking Using Topic RelevanceIEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences10.1587/transfun.2020SMP0023E103.A:12(1529-1540)Online publication date: 1-Dec-2020
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      cover image ACM Conferences
      MIR '08: Proceedings of the 1st ACM international conference on Multimedia information retrieval
      October 2008
      506 pages
      ISBN:9781605583129
      DOI:10.1145/1460096
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      Publication History

      Published: 30 October 2008

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      Author Tags

      1. ambiguity
      2. diversity
      3. flickr
      4. image retrieval
      5. pseudo-relevance feedback
      6. retrieval performance

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      October 30 - 31, 2008
      British Columbia, Vancouver, Canada

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      View all
      • (2024)Offline Evaluation of Set-Based Text-to-Image GenerationProceedings of the 2024 Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region10.1145/3673791.3698424(42-53)Online publication date: 8-Dec-2024
      • (2022)An overview of cluster-based image search result organization: background, techniques, and ongoing challengesKnowledge and Information Systems10.1007/s10115-021-01650-9Online publication date: 11-Feb-2022
      • (2020)Heterogeneous-Graph-Based Video Search Reranking Using Topic RelevanceIEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences10.1587/transfun.2020SMP0023E103.A:12(1529-1540)Online publication date: 1-Dec-2020
      • (2016)User Intent in Multimedia SearchACM Computing Surveys10.1145/295493049:2(1-37)Online publication date: 13-Aug-2016
      • (2016)Web video topics discovery and structuralization with social networkNeurocomputing10.1016/j.neucom.2014.10.103172:C(53-63)Online publication date: 8-Jan-2016
      • (2016)Exploiting visual saliency for increasing diversity of image retrieval resultsMultimedia Tools and Applications10.1007/s11042-015-2526-475:10(5581-5602)Online publication date: 1-May-2016
      • (2016)Integrating multiple types of features for event identification in social imagesMultimedia Tools and Applications10.1007/s11042-014-2436-x75:6(3301-3322)Online publication date: 1-Mar-2016
      • (2015)Image Search Reranking With Hierarchical Topic AwarenessIEEE Transactions on Cybernetics10.1109/TCYB.2014.236674045:10(2177-2189)Online publication date: Oct-2015
      • (2014)Diversity-driven learning for multimodal image retrieval with relevance feedback2014 IEEE International Conference on Image Processing (ICIP)10.1109/ICIP.2014.7025445(2197-2201)Online publication date: Oct-2014
      • (2013)Social Ties and User Content Generation: Evidence from FlickrInformation Systems Research10.1287/isre.1120.046424:1(71-87)Online publication date: Mar-2013
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