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Image Classification Using Optimized Synergetic Neural Network

  • Conference paper
Book cover Intelligent Robotics Systems: Inspiring the NEXT (FIRA 2013)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 376))

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Abstract

In this paper an empirical study on image classification using optimized synergetic neural network is conducted. Unbalanced attention parameter type of synergetic neural network is enhanced by applying optimization algorithms such as particle swarm algorithm, quantum particle swarm algorithm and Shuffled Complex Evolution with PCA. In this work Gabor-wavelet algorithm has been applied in the feature extraction stage. The ABM image dataset consisting of four classes of Bear, Cat, Cow and Wolf is used and divided into two groups of training and test sets. The aim of this empirical work is to optimize unbalanced attention parameters of synergetic neural network, to achieve the highest accuracy of image classification. Results are calculated after applying three-fold cross validation. According to the results, optimized synergetic neural network performed better than the simple synergetic neural network. The highest classification results obtained using SNN-QPSO, which is 0.94.

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Author information

Authors and Affiliations

  1. Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia

    Ghazaleh Taherzadeh & Chu Kiong Loo

Authors
  1. Ghazaleh Taherzadeh
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  2. Chu Kiong Loo
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Editor information

Editors and Affiliations

  1. Center for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, 43600, Bangi, Selangor, Malaysia

    Khairuddin Omar  & Mohammad Faidzul Nasrudin  & 

  2. Center for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, 43600 UKM, Bangi, Selangor, Malaysia

    Md Jan Nordin

  3. National University of Singapore, Singapore

    Prahlad Vadakkepat

  4. Center for Artificial Intelligence Technology (CAIT), Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, 43600 UKM, Bangi, Selangor D.E., Malaysia

    Anton Satria Prabuwono  & Siti Norul Huda Sheikh Abdullah  & 

  5. Autonomous Agents Lab, University of Manitoba, R3T 2N2, Winnipeg, Manitoba, Canada

    Jacky Baltes

  6. Center for Artificial Intelligence and Robotics (CAIRO), Fakulti Kejuruteraan Elektrik, Universiti Teknologi Malaysia, 81310 UTM, Skudai, Johor, Malaysia

    Shamsudin Mohd Amin

  7. Department of Electrical & Electronics Engineering, Faculty of Engineering, University Putra Malaysia, 43400 UPM, Serdang, Selangor

    Wan Zuha Wan Hassan

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© 2013 Springer-Verlag Berlin Heidelberg

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Taherzadeh, G., Loo, C.K. (2013). Image Classification Using Optimized Synergetic Neural Network. In: Omar, K., et al. Intelligent Robotics Systems: Inspiring the NEXT. FIRA 2013. Communications in Computer and Information Science, vol 376. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40409-2_15

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  • DOI: https://doi.org/10.1007/978-3-642-40409-2_15

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-40408-5

  • Online ISBN: 978-3-642-40409-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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