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.
翻译:我们提出了一个基于检索的系统,用于里程碑的检索和识别挑战。在检索竞争系统中,有五个部分,包括地物提取和匹配以获得候选人的排队;数据库扩增和查询扩展搜索;从确认结果和本地特征匹配中重新排序。在承认挑战中,包括:里程碑和非地标确认、多重确认结果表决,以及结合确认和检索结果进行重新排序。所有模型都由PaddlePadddle框架培训和预测。我们使用我们的方法,在Google Landmark 识别2019和Google Landmark Retrievval 2019年卡格格勒第二名中取得了第二名。