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Content-based image retrieval by combining convolutional neural networks and sparse representation

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Abstract

As stored data and images on memory disks increase, image retrieval has a necessary task on image processing. Although lots of researches have been reported for this task so far, semantic gap between low level features of images and human concept is still an important challenge on content-based image retrieval. For this task, a robust method is proposed by a combination of convolutional neural network and sparse representation, in which deep features are extracted by using CNN and sparse representation to increase retrieval speed and accuracy. The proposed method has been tested on three common databases on image retrieval, named Corel, ALOI and MPEG7. By computing metrics such as P(0.5), P(1) and ANMRR, experimental results show that the proposed method has achieved higher accuracy and better speed compared to state-of-the-art methods.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Birjand, Shahid Avini Highway, Birjand, Iran

    Amir Sezavar, Hassan Farsi & Sajad Mohamadzadeh

Authors
  1. Amir Sezavar
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  2. Hassan Farsi
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  3. Sajad Mohamadzadeh
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Correspondence to Hassan Farsi.

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Sezavar, A., Farsi, H. & Mohamadzadeh, S. Content-based image retrieval by combining convolutional neural networks and sparse representation. Multimed Tools Appl 78, 20895–20912 (2019). https://doi.org/10.1007/s11042-019-7321-1

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  • DOI: https://doi.org/10.1007/s11042-019-7321-1

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