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Adaptive Image Classification for Aerial Photo Image Retrieval

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AI 2004: Advances in Artificial Intelligence (AI 2004)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3339))

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Abstract

The paper presents a content based image retrieval approach with adaptive and intelligent image classification through on-line model modification. It supports geographical image retrieval over digitized historical aerial photographs in a digital library. Since the historical aerial photographs are grayscaled and low-resolution images, image retrieval is achieved on the basis of texture feature extraction. Feature extraction methods for geographical image retrieval are Gabor spectral filtering, Laws’ energy filtering, and Wavelet transformation, which are all the most widely used in image classification and segmentation. Adaptive image classification supports effective content based image retrieval through composite classifier models dealing with multi-modal feature distribution. The image retrieval methods presented in the paper are evaluated over a test bed of 184 aerial photographs. The experimental results also show the performance of different feature extraction methods for each image retrieval method.

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Author information

Authors and Affiliations

  1. College of Electronics and Information Engineering, Sejong University, Seoul, 143-747, Korea

    Sung Wook Baik

  2. Department of Computer Engineering, Honam University, Gwangju, 506-090, Korea

    Ran Baik

Authors
  1. Sung Wook Baik
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  2. Ran Baik
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Editor information

Editors and Affiliations

  1. Faculty of Information Technology, Monash University, VIC 3800, Australia

    Geoffrey I. Webb

  2. Science, Engineering and Technology Portfolio, Royal Melbourne Institute of Technology, VIC 3001, Melbourne, Australia

    Xinghuo Yu

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© 2004 Springer-Verlag Berlin Heidelberg

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Baik, S.W., Baik, R. (2004). Adaptive Image Classification for Aerial Photo Image Retrieval. In: Webb, G.I., Yu, X. (eds) AI 2004: Advances in Artificial Intelligence. AI 2004. Lecture Notes in Computer Science(), vol 3339. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30549-1_12

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  • DOI: https://doi.org/10.1007/978-3-540-30549-1_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-24059-4

  • Online ISBN: 978-3-540-30549-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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