Triplet Label Based Image Retrieval Using Deep Learning in Large Database

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 spatialattention 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