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Query Difficulty Guided Image Retrieval System

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Advances in Multimedia Modeling (MMM 2011)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 6524))

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Abstract

Query difficulty estimation is a useful tool for content-based image retrieval. It predicts the performance of the search result of a given query, and thus it can guide the pseudo relevance feedback to rerank the image search results, and can be used to re-write the given query by suggesting “easy” alternatives. This paper presents a query difficulty estimation guided image retrieval system. The system initially estimates the difficulty of a given query image by analyzing both the query image and the retrieved top ranked images. Different search strategies are correspondingly applied to improve the retrieval performance.

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Author information

Authors and Affiliations

  1. Key Laboratory of Machine Perception, Peking University, Beijing

    Yangxi Li, Yong Luo & Chao Xu

  2. School of Computer Engineering, Nanyang Technological University, Singapore

    Dacheng Tao

Authors
  1. Yangxi Li
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  2. Yong Luo
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  3. Dacheng Tao
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  4. Chao Xu
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Editor information

Editors and Affiliations

  1. National Taiwan Ocean University, Keelung, Taiwan

    Kuo-Tien Lee  & Jun-Wei Hsieh  & 

  2. National Chiao Tung University, Hsinchu, Taiwan

    Wen-Hsiang Tsai

  3. Academia Sinica, Taipei, Taiwan

    Hong-Yuan Mark Liao

  4. Cornell University, Ithaca, NY, USA

    Tsuhan Chen

  5. National Kaohsiung First University of Science and Technology, Kaohsiung, Taiwan

    Chien-Cheng Tseng

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© 2011 Springer-Verlag Berlin Heidelberg

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Li, Y., Luo, Y., Tao, D., Xu, C. (2011). Query Difficulty Guided Image Retrieval System. In: Lee, KT., Tsai, WH., Liao, HY.M., Chen, T., Hsieh, JW., Tseng, CC. (eds) Advances in Multimedia Modeling. MMM 2011. Lecture Notes in Computer Science, vol 6524. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17829-0_46

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  • DOI: https://doi.org/10.1007/978-3-642-17829-0_46

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-17828-3

  • Online ISBN: 978-3-642-17829-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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