Visual place recognition using query and database images from different sources remains a challenging task in computer vision. Our method exploits global descriptors for efficient image matching and local descriptors for geometric verification. We present a novel, multi-scale aggregation method for local convolutional descriptors, using memory vector construction for efficient aggregation. The method enables to find preliminary set of image candidate matches and remove visually similar but erroneous candidates. We deploy the multi-scale aggregation for visual place recognition on 3 large-scale datasets. We obtain a Recall@10 larger than 94% for the Pittsburgh dataset, outperforming other popular convolutional descriptors used in image retrieval and place recognition. Additionally, we provide a comparison for these descriptors on a more challenging dataset containing query and database images obtained from different sources, achieving over 77% Recall@10.
|Number of pages||7|
|Journal||IS and T International Symposium on Electronic Imaging Science and Technology|
|Publication status||Published - 26 Jan 2020|
|Event||18th Image Processing: Algorithms and Systems Conference, IPAS 2020 - Burlingame, United States|
Duration: 26 Jan 2020 → 30 Jan 2020