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Natural Scene Retrieval Based on Graph Semantic Similarity for Adaptive Scene Classification

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Computational Collective Intelligence. Semantic Web, Social Networks and Multiagent Systems (ICCCI 2009)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5796))

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Abstract

In this paper, we introduce our method for image retrieval to access and measuring the similarity of natural scenes by using graph semantic similarity. The proposed method is motivated by continuing effort from our previous work in adaptive image classification based on semantic concepts and edge detection. The method will learn the image information by concept occurrence vector of semantic concepts such as water, grass, sky and foliage. We constructed the graph using this information and illustrate the similarity with connecting edges. The empirical results demonstrated promising performance in terms of accuracy.

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Author information

Authors and Affiliations

  1. Department of Computer and Information Engineering, Inha University, 253 Yonghyun-dong, Incheon, Korea, 402-751

    Nuraini Jamil & Sanggil Kang

Authors
  1. Nuraini Jamil
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  2. Sanggil Kang
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Editor information

Editors and Affiliations

  1. Institute of Informatics, Wroclaw University of Technology, Str. Janiszweskiego 11/17, 50-370, Wroclaw, Poland

    Ngoc Thanh Nguyen

  2. Centre for Complex Software Systems and Services, Swinburne University of Technology, Hawthorn, P.O. Box 218, 3122, Victoria, Australia

    Ryszard Kowalczyk

  3. Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, G#43, Sec. 4, Keelung Rd, 106, Taipei, Taiwan, R.O.C.

    Shyi-Ming Chen

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© 2009 Springer-Verlag Berlin Heidelberg

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Jamil, N., Kang, S. (2009). Natural Scene Retrieval Based on Graph Semantic Similarity for Adaptive Scene Classification. In: Nguyen, N.T., Kowalczyk, R., Chen, SM. (eds) Computational Collective Intelligence. Semantic Web, Social Networks and Multiagent Systems. ICCCI 2009. Lecture Notes in Computer Science(), vol 5796. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04441-0_59

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  • DOI: https://doi.org/10.1007/978-3-642-04441-0_59

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-04440-3

  • Online ISBN: 978-3-642-04441-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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