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Triplet Label Based Image Retrieval Using Deep Learning in Large Database

K. Nithya1,*, V. Rajamani2
1 Research Scholar, Department of Information and Communication Engineering, Anna University, Chennai, 600025, India
2 Department of Electronics and Communication Engineering, Veltech Multitech Dr. Rangarajan Dr. Sakunthala Engineering College, Chennai, 600062, India
* Corresponding Author: K. Nithya. Email:

Computer Systems Science and Engineering 2023, 44(3), 2655-2666. https://doi.org/10.32604/csse.2023.027275

Received 13 January 2022; Accepted 23 February 2022; Issue published 01 August 2022

Abstract

Recent days, Image retrieval has become a tedious process as the image database has grown very larger. The introduction of Machine Learning (ML) and Deep Learning (DL) made this process more comfortable. In these, the pair-wise label similarity is used to find the matching images from the database. But this method lacks of limited propose code and weak execution of misclassified images. In order to get-rid of the above problem, a novel triplet based label that incorporates context-spatial similarity measure is proposed. A Point Attention Based Triplet Network (PABTN) is introduced to study propose code that gives maximum discriminative ability. To improve the performance of ranking, a correlating resolutions for the classification, triplet labels based on findings, a spatial-attention mechanism and Region Of Interest (ROI) and small trial information loss containing a new triplet cross-entropy loss are used. From the experimental results, it is shown that the proposed technique exhibits better results in terms of mean Reciprocal Rank (mRR) and mean Average Precision (mAP) in the CIFAR-10 and NUS-WIPE datasets.

Keywords

Image retrieval; deep learning; point attention based triplet network; correlating resolutions; classification; region of interest

Cite This Article

K. Nithya and V. Rajamani, "Triplet label based image retrieval using deep learning in large database," Computer Systems Science and Engineering, vol. 44, no.3, pp. 2655–2666, 2023.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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