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Soft Computing for Problem Solving pp 573–583Cite as

Indoor Object Classification Using Higher Dimensional MPEG Features

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1048))

Abstract

We propose a generic model to classify a given image by detecting a specific image patch. Employing MPEG-7 features along with feature selection populates the feature space which is used to train using SVM classier. Our work target toward classifying objects in an unclassified image. We propose a model that can detect objects through generic framework for larger classes. Our model gives an overall accuracy of over 97%.

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Author information

Authors and Affiliations

  1. Department of Computer Science, Graphic Era Deemed to Be University, Dehradun, India

    Dibyendu Roy Chaudhuri, Dhairya Chandra & Ankush Mittal

Authors
  1. Dibyendu Roy Chaudhuri
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  2. Dhairya Chandra
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  3. Ankush Mittal
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Corresponding author

Correspondence to Dibyendu Roy Chaudhuri .

Editor information

Editors and Affiliations

  1. Department of Mathematics, National Institute of Technology Silchar, Silchar, Assam, India

    Kedar Nath Das

  2. Department of Mathematics, South Asian University, New Delhi, Delhi, India

    Jagdish Chand Bansal

  3. Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

    Kusum Deep

  4. Department of Mathematics, Faculty of Science, Liverpool Hope University, Liverpool, UK

    Atulya K. Nagar

  5. School of Electrical Engineering, VIT University, Vellore, Tamil Nadu, India

    Ponnambalam Pathipooranam

  6. School of Electrical Engineering, VIT University, Vellore, Tamil Nadu, India

    Rani Chinnappa Naidu

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Chaudhuri, D.R., Chandra, D., Mittal, A. (2020). Indoor Object Classification Using Higher Dimensional MPEG Features. In: Das, K., Bansal, J., Deep, K., Nagar, A., Pathipooranam, P., Naidu, R. (eds) Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 1048. Springer, Singapore. https://doi.org/10.1007/978-981-15-0035-0_47

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