Image Geolocation Method Based on Attention Mechanism Front Loading and Feature Fusion. (14th June 2022)

• Record Type: Journal Article
• Authors: Lu, Huayuan; Yang, Chunfang; Qi, Baojun; Zhu, Ma; Xu, Jingqian
• Other Names: Cianca Ernestina Academic Editor.
• Is Part Of: Wireless communications and mobile computing. Volume 2022(2022)
• Publication Date: 2022-06-14
• Subjects: Wireless communication systems -- Periodicals; Mobile communication systems -- Periodicals; 621.38205
• DOI: 10.1155/2022/7168451
• Languages: English
• ISSNs: 1530-8669

Abstract

Abstract : Image geolocation is an important technique for robotics and autonomous systems. The existing methods mainly extract local features from images directly and use global descriptors, which are aggregated by these local features, to retrieve candidate references from all references. Thus, the training efficiency is affected by the image noises and the accuracy is so limited that the further verification is extremely time consuming. To address these issues, this work proposes an image geolocation framework, which adds the noise filtering layer before local feature extraction. Based on this framework, an image geolocation method based on attention mechanism front loading and feature fusion is designed. In the noise filtering layer, the proposed method uses triplet attention to denoise images thus leading to higher training efficiency. In the feature aggregation layer, an improved SPP (spatial pyramid pooling) is designed to extract the local factors reflected by the position relationships among local features. Then, the local factors are incorporated with the global factors extracted by NetVLAD. The fused descriptors contain not only the statistic of the geometric elements but also the position relationships among them. The experimental results show that the proposed method outperforms NetVLAD in terms of the training convergence round and Recall@N (N = 1, 5, 10, 20 ); especially, the convergence round of Recall@5 reduces from 25 to 10, the convergence round of Recall@10 reduces from 25 to 7, Recall@1 increases from 79.45% to 84.01%, and Recall@5 increases from 90.10% to 92.81%.