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Cluster Computing

Erschienen in:

07.02.2018

Content based image retrieval using deep learning process

verfasst von: R. Rani Saritha, Varghese Paul, P. Ganesh Kumar

Erschienen in: Cluster Computing | Sonderheft 2/2019

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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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Titel: Content based image retrieval using deep learning process
verfasst von: R. Rani Saritha
Varghese Paul
P. Ganesh Kumar
Publikationsdatum: 07.02.2018
Verlag: Springer US
Erschienen in: Cluster Computing / Ausgabe Sonderheft 2/2019
Print ISSN: 1386-7857
Elektronische ISSN: 1573-7543
DOI: https://doi.org/10.1007/s10586-018-1731-0

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