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Image Classification Based on Inception-v3 and a Mixture of Handcrafted Features

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Distributed Computing and Optimization Techniques

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 903))

Abstract

Annotating, retrieving and classifying images in an ever increasing large image datasets with semantic information is still an exigent task. Image pre-processing plays a vital role in partitioning an image into small meaningful regions and extracting features from these regions can be used to stimulate the learning models for better and accurate image classification. Deeplabv3+ framework based on dilated convolution is used to separate out the semantic regions and Histogram of Oriented Gradients (HOG) technique is applied to these regions and computed the distribution of gradients as features. A comparative analysis and examination of various Machine Learning classifiers using edge based, semantically segmented features are compared against the state-of-the-art deep learning techniques. The proposed deep learning based Inception-v3 architecture enables dynamic object recognition with factorized convolution and parameter reduction. The proposed model outperforms hand crafted ML classifiers, shows a significant performance improvement on much diverse dataset like Caltech-256 in comparison with Caltech 101.

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Authors and Affiliations

  1. Department of ECE, SJB Institute of Technology, Bangalore, India

    A. Shubha Rao & K. Mahantesh

Authors
  1. A. Shubha Rao
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  2. K. Mahantesh
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Corresponding author

Correspondence to A. Shubha Rao .

Editor information

Editors and Affiliations

  1. Electrical Engineering, Indian Institute of Technology Patna, Patna, Bihar, India

    Sudhan Majhi

  2. Telecommunication Engineering, University of Jaén, Jaén, Spain

    Rocío Pérez de Prado

  3. Electronics and Communication, SJB Institute of Technology, Bengaluru, Karnataka, India

    Chandrappa Dasanapura Nanjundaiah

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Shubha Rao, A., Mahantesh, K. (2022). Image Classification Based on Inception-v3 and a Mixture of Handcrafted Features. In: Majhi, S., Prado, R.P.d., Dasanapura Nanjundaiah, C. (eds) Distributed Computing and Optimization Techniques. Lecture Notes in Electrical Engineering, vol 903. Springer, Singapore. https://doi.org/10.1007/978-981-19-2281-7_49

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  • DOI: https://doi.org/10.1007/978-981-19-2281-7_49

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-2280-0

  • Online ISBN: 978-981-19-2281-7

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