基于关键点匹配的快速动物个体识别算法，支持多物种、大规模数据库
HotSpotter - Patterned Species Instance Recognition
- 通过提取并匹配图像中的关键特征点（热点）进行识别
- 在千级数据库上实现秒级响应，准确率优于已有方法
- 适用于斑马、长颈鹿、猎豹等多类动物，无需物种定制
我们提出 HotSpotter，一种快速且准确的个体动物识别算法，不依赖特定物种，已成功应用于格雷维斑马、平原斑马、长颈鹿、猎豹和狮子鱼。该算法采用两种基于关键点（热点）提取与匹配的方法：第一种对查询图像逐个比对数据库图像，独立计算得分并排序；第二种基于近期实例识别技术，利用快速最近邻搜索，结合局部朴素贝叶斯最近邻算法的竞争力评分机制。实验在超过1000张图像的数据库上验证，结果表明其识别准确率高于现有方法，且每张查询图像可在数秒内完成匹配。
原文摘要 · Abstract (English)
We present HotSpotter, a fast, accurate algorithm for identifying individual animals against a labeled database. It is not species specific and has been applied to Grevy's and plains zebras, giraffes, leopards, and lionfish. We describe two approaches, both based on extracting and matching keypoints or "hotspots". The first tests each new query image sequentially against each database image, generating a score for each database image in isolation, and ranking the results. The second, building on recent techniques for instance recognition, matches the query image against the database using a fast nearest neighbor search. It uses a competitive scoring mechanism derived from the Local Naive Bayes Nearest Neighbor algorithm recently proposed for category recognition. We demonstrate results on databases of more than 1000 images, producing more accurate matches than published methods and matching each query image in just a few seconds.
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