Authors: Jatothu Brahmaiah Naik; Siva Nagi Reddy Kalli; Ravi Boda
Addresses: Department of ECE, Kallam Haranadhareddy Institute of Technology, Guntur, Andhra, Pradesh, 522616, India ' Department of Electronics and Communication Engineering, Sridevi Womens Engineering College, Hyderabad, Telangana – 500075, India ' KLEF Deemed to be University Off-Campus, Hyderabad, Aziz Nagar, Hyderabad, Telangana – 500075, India
Abstract: Currently, the term 'content-based image retrieval' seems to be a highly attentive system for handling the broad image datasets since the data storage mechanisms and image acquisition are becoming the most empowered logic in image processing. The previous CBIR system has been proposed under nonlinear similarity matching measure in a logarithmic scale and informative pattern descriptor has quantified the range of similarity content. This article implements a novel CBIR system that emphasises the classification concept using a deep belief network (DBN) classifier. In this concept, apart from the image retrieval, the used classifier classifies the respective classes of retrieved images. Finally, the proposed local vector pattern (PLVP) with DBN classifier (PLVP-DBN) compares its performance over other conventional retrieval concepts: PLVP-with log similarity, PLVP-without log similarity, and also with neural network (NN) classifier.
Keywords: image retrieval; CBIR system; deep belief network; DBN; NN; PLVP-DBN.
International Journal of Ad Hoc and Ubiquitous Computing, 2023 Vol.42 No.4, pp.226 - 242
Received: 26 Aug 2021
Received in revised form: 07 May 2022
Accepted: 16 May 2022
Published online: 21 Apr 2023 *