12 papers with code • 2 benchmarks • 3 datasets
GLDv2 is the largest such dataset to date by a large margin, including over 5M images and 200k distinct instance labels.
We present a retrieval based system for landmark retrieval and recognition challenge. There are five parts in retrieval competition system, including feature extraction and matching to get candidates queue; database augmentation and query extension searching; reranking from recognition results and local feature matching.
Due to the variance of the images, which include extreme viewpoint changes such as having to retrieve images of the exterior of a landmark from images of the interior, this is very challenging for approaches based exclusively on visual similarity.
It allows to drastically reduce the query time and outperforms the accuracy results compared to the state-of-the-art methods for large-scale landmark recognition.
The landmark recognition problem is far from being solved, but with the use of features extracted from intermediate layers of Convolutional Neural Networks (CNNs), excellent results have been obtained.
Image Landmark Recognition has been one of the most sought-after classification challenges in the field of vision and perception.
Besides, we devise a discriminative re-ranking method to address the diversity of the dataset for landmark retrieval.
This article presents an efficient end-to-end method to perform instance-level recognition employed to the task of labeling and ranking landmark images.