2nd Place and 2nd Place Solution to Kaggle Landmark Recognition andRetrieval Competition 2019
This work addresses landmark identification for computer vision applications, but it is incremental as it builds on existing retrieval and recognition methods.
The authors tackled the landmark recognition and retrieval challenge by developing a system with feature extraction, database augmentation, and reranking, achieving 2nd place in both the Google Landmark Recognition 2019 and Google Landmark Retrieval 2019 competitions on Kaggle.
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. In recognition challenge including: landmark and non-landmark recognition, multiple recognition results voting and reranking using combination of recognition and retrieval results. All of models trained and predicted by PaddlePaddle framework. Using our method, we achieved 2nd place in the Google Landmark Recognition 2019 and 2nd place in the Google Landmark Retrieval 2019 on kaggle. The source code is available at here.