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Feature synthesis for image classification and retrieval via one-against-all perceptrons

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Abstract

Most existing content-based image retrieval and classification systems rely on low-level features which are automatically extracted from images. However, often these features lack the discrimination power needed for accurate description of the image content, and hence, they may lead to a poor retrieval or classification performance. We propose a novel technique to improve low-level features which uses parallel one-against-all perceptrons to synthesize new features with a higher discrimination power which in turn leads to improved classification and retrieval results. The proposed method can be applied on any database and low-level features as long as some ground-truth information is available. The main merits of the proposed technique are its simplicity and faster computation compared to existing feature synthesis methods. Extensive simulation results show a significant improvement in the features’ discrimination power.

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Authors and Affiliations

  1. Department of Signal Processing, Tampere University of Technology, PO Box 527, 33101, Tampere, Finland

    Jenni Raitoharju & Moncef Gabbouj

  2. Electrical Engineering Department, College of Engineering, Qatar University, Doha, Qatar

    Serkan Kiranyaz

Authors
  1. Jenni Raitoharju
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  2. Serkan Kiranyaz
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  3. Moncef Gabbouj
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Correspondence to Jenni Raitoharju.

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Raitoharju, J., Kiranyaz, S. & Gabbouj, M. Feature synthesis for image classification and retrieval via one-against-all perceptrons. Neural Comput & Applic 29, 943–957 (2018). https://doi.org/10.1007/s00521-016-2504-4

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  • DOI: https://doi.org/10.1007/s00521-016-2504-4

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