Open Access

ARTICLE

DIEONet: Domain-Invariant Information Extraction and Optimization Network for Visual Place Recognition

Shaoqi Hou^1,2,3,*, Zebang Qin², Chenyu Wu², Guangqiang Yin², Xinzhong Wang¹, Zhiguo Wang^2,*

1 School of Computer Science and Technology, Xinjiang University, Urumqi, 830046, China
2 School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China
3 Institute of Public Security, Kash Institute of Electronics and Information Industry, Kashi, 844000, China

* Corresponding Authors: Shaoqi Hou. Email: ; Zhiguo Wang. Email:

Computers, Materials & Continua 2025, 82(3), 5019-5033. https://doi.org/10.32604/cmc.2025.058233

Received 07 September 2024; Accepted 11 November 2024; Issue published 06 March 2025

Abstract

Visual Place Recognition (VPR) technology aims to use visual information to judge the location of agents, which plays an irreplaceable role in tasks such as loop closure detection and relocation. It is well known that previous VPR algorithms emphasize the extraction and integration of general image features, while ignoring the mining of salient features that play a key role in the discrimination of VPR tasks. To this end, this paper proposes a Domain-invariant Information Extraction and Optimization Network (DIEONet) for VPR. The core of the algorithm is a newly designed Domain-invariant Information Mining Module (DIMM) and a Multi-sample Joint Triplet Loss (MJT Loss). Specifically, DIMM incorporates the interdependence between different spatial regions of the feature map in the cascaded convolutional unit group, which enhances the model’s attention to the domain-invariant static object class. MJT Loss introduces the “joint processing of multiple samples” mechanism into the original triplet loss, and adds a new distance constraint term for “positive and negative” samples, so that the model can avoid falling into local optimum during training. We demonstrate the effectiveness of our algorithm by conducting extensive experiments on several authoritative benchmarks. In particular, the proposed method achieves the best performance on the TokyoTM dataset with a Recall@1 metric of 92.89%.

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Keywords

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