Triplet Label Based Image Retrieval Using Deep Learning in Large Database  

作　　者：K.Nithya V.Rajamani 

机构地区：[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

出　　处：《Computer Systems Science & Engineering》2023年第3期2655-2666,共12页计算机系统科学与工程（英文）

摘　　要：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 tofind 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 corre-lating resolutions for the classification,triplet labels based onfindings,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.

关 键 词：Image retrieval deep learning point attention based triplet network correlating resolutions classification region of interest 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]