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Content based image retrieval using deep learning process

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Abstract

Content-based image retrieval (CBIR) uses image content features to search and retrieve digital images from a large database. A variety of visual feature extraction techniques have been employed to implement the searching purpose. Due to the computation time requirement, some good algorithms are not been used. The retrieval performance of a content-based image retrieval system crucially depends on the feature representation and similarity measurements. The ultimate aim of the proposed method is to provide an efficient algorithm to deal with the above mentioned problem definition. Here the deep belief network (DBN) method of deep learning is used to extract the features and classification and is an emerging research area, because of the generation of large volume of data. The proposed method is tested through simulation in comparison and the results show a huge positive deviation towards its performance.

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Authors and Affiliations

  1. Department of Computer Applications, Saintgits College of Engineering, Kottayam, Kerala, India

    R. Rani Saritha

  2. Department of CS/IT, TocH Institute of Science & Techology, Ernakulam, Kerala, India

    Varghese Paul

  3. Department of Information Technology, Anna University of Technology, Coimbatore, Tamil Nadu, India

    P. Ganesh Kumar

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  1. R. Rani Saritha
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  2. Varghese Paul
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  3. P. Ganesh Kumar
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Correspondence to R. Rani Saritha.

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Saritha, R.R., Paul, V. & Kumar, P.G. Content based image retrieval using deep learning process. Cluster Comput 22 (Suppl 2), 4187–4200 (2019). https://doi.org/10.1007/s10586-018-1731-0

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