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Weather-Adaptive Distance Metric for Landmark Image Classification

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Advances in Multimedia Information Processing -- PCM 2015 (PCM 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9315))

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Abstract

Visual appearance of landmark photos changes significantly in different weather conditions. In this work, we obtain weather information from a weather forecast website based on a landmark photo’s geotag and taken time information. With weather information, we adaptively adjust weightings for combining distances obtained based on different features and thus propose a weather-adaptive distance measure for landmark photo classification. We verify the effectiveness of this idea, and accomplish one of the early attempts to develop a landmark photo classification system that resists to weather changes.

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Notes

  1. 1.

    Weather Underground, http://www.wunderground.com/.

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Acknowledgements

The work was partially supported by the Ministry of Science and Technology in Taiwan under the grant MOST103-2221-E-194-027-MY3.

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Authors and Affiliations

  1. National Chung Cheng University, Chiayi County, Taiwan

    Ding-Shiuan Ding & Wei-Ta Chu

Authors
  1. Ding-Shiuan Ding
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  2. Wei-Ta Chu
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Corresponding author

Correspondence to Wei-Ta Chu .

Editor information

Editors and Affiliations

  1. Gwangju Institute of Science and Technology, Gwangju, Korea (Republic of)

    Yo-Sung Ho

  2. Chinese Academy of Sciences, Institute of Automation, Beijing, China

    Jitao Sang

  3. KAIST, Daejeon, Korea (Republic of)

    Yong Man Ro

  4. KAIST, Daejeon, Korea (Republic of)

    Junmo Kim

  5. College of Computer Science, Zhejiang University, Hangzhou, China

    Fei Wu

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Ding, DS., Chu, WT. (2015). Weather-Adaptive Distance Metric for Landmark Image Classification. In: Ho, YS., Sang, J., Ro, Y., Kim, J., Wu, F. (eds) Advances in Multimedia Information Processing -- PCM 2015. PCM 2015. Lecture Notes in Computer Science(), vol 9315. Springer, Cham. https://doi.org/10.1007/978-3-319-24078-7_14

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  • DOI: https://doi.org/10.1007/978-3-319-24078-7_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-24077-0

  • Online ISBN: 978-3-319-24078-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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