{"title":"Pyramid transformer-based triplet hashing for robust visual place recognition","authors":"Zhenyu Li , Pengjie Xu","doi":"10.1016/j.cviu.2024.104167","DOIUrl":null,"url":null,"abstract":"<div><p>Deep hashing is being used to approximate nearest neighbor search for large-scale image recognition problems. However, CNN architectures have dominated similar applications. We present a Pyramid Transformer-based Triplet Hashing architecture to handle large-scale place recognition challenges in this study, leveraging the capabilities of Vision Transformer (ViT). For feature representation, we create a Siamese Pyramid Transformer backbone. We present a multi-scale feature aggregation technique to learn discriminative features for scale-invariant features. In addition, we observe that binary codes suitable for place recognition are sub-optimal. To overcome this issue, we use a self-restraint triplet loss deep learning network to create compact hash codes, further increasing recognition accuracy. To the best of our knowledge, this is the first study to use a triplet loss deep learning network to handle the deep hashing learning problem. We do extensive experiments on four difficult place datasets: KITTI, Nordland, VPRICE, and EuRoC. The experimental findings reveal that the suggested technique performs at the cutting edge of large-scale visual place recognition challenges.</p></div>","PeriodicalId":50633,"journal":{"name":"Computer Vision and Image Understanding","volume":null,"pages":null},"PeriodicalIF":4.3000,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computer Vision and Image Understanding","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1077314224002480","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
Deep hashing is being used to approximate nearest neighbor search for large-scale image recognition problems. However, CNN architectures have dominated similar applications. We present a Pyramid Transformer-based Triplet Hashing architecture to handle large-scale place recognition challenges in this study, leveraging the capabilities of Vision Transformer (ViT). For feature representation, we create a Siamese Pyramid Transformer backbone. We present a multi-scale feature aggregation technique to learn discriminative features for scale-invariant features. In addition, we observe that binary codes suitable for place recognition are sub-optimal. To overcome this issue, we use a self-restraint triplet loss deep learning network to create compact hash codes, further increasing recognition accuracy. To the best of our knowledge, this is the first study to use a triplet loss deep learning network to handle the deep hashing learning problem. We do extensive experiments on four difficult place datasets: KITTI, Nordland, VPRICE, and EuRoC. The experimental findings reveal that the suggested technique performs at the cutting edge of large-scale visual place recognition challenges.
期刊介绍:
The central focus of this journal is the computer analysis of pictorial information. Computer Vision and Image Understanding publishes papers covering all aspects of image analysis from the low-level, iconic processes of early vision to the high-level, symbolic processes of recognition and interpretation. A wide range of topics in the image understanding area is covered, including papers offering insights that differ from predominant views.
Research Areas Include:
• Theory
• Early vision
• Data structures and representations
• Shape
• Range
• Motion
• Matching and recognition
• Architecture and languages
• Vision systems