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A Novel Object Categorization Model with Implicit Local Spatial Relationship

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Part of the book series:Lecture Notes in Computer Science ((LNTCS,volume 6064))

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Abstract

Object categorization is an important problem in computer vision. The bag-of-words approach has gained much research in object categorization, which has shown state-of-art performance. This bag-of-words(BOW) approach ignores spatial relationship between local features. But local features in most classes have spatial dependence in real world. So we propose a novel object categorization model with implicit local spatial relationship based on bag-of-words model(BOW with ILSR). The model use neighbor features of one local feature as its implicit local spatial relationship, which is integrated with its appearance feature to form two sources of information for categorization. The characteristic of the model can not only preserve some degree of flexibility, but also incorporate necessary spatial information. The algorithm is applied in Caltech-101 and Caltech-256 datasets to validate its efficiency. The experimental results show its good performance.

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Author information

Authors and Affiliations

  1. School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China

    Lina Wu, Siwei Luo & Wei Sun

Authors
  1. Lina Wu

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  2. Siwei Luo

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  3. Wei Sun

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Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Shanghai Jiao Tong University, 800, Dongchuan Road, 200240, Shanghai, China

    Liqing Zhang  & Bao-Liang Lu  & 

  2. Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Clear Water bay, Kowloon, Hong Kong, China

    James Kwok

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© 2010 Springer-Verlag Berlin Heidelberg

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Wu, L., Luo, S., Sun, W. (2010). A Novel Object Categorization Model with Implicit Local Spatial Relationship. In: Zhang, L., Lu, BL., Kwok, J. (eds) Advances in Neural Networks - ISNN 2010. ISNN 2010. Lecture Notes in Computer Science, vol 6064. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13318-3_18

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