We address the problem of identifying individual cetaceans from images
showing the trailing edge of their fins. Given the trailing edge from an
unknown individual, we produce a ranking of known individuals from a database.
The nicks and notches along the trailing edge define an individual's unique
signature. We define a representation based on integral curvature that is
robust to changes in viewpoint and pose, and captures the pattern of nicks and
notches in a local neighborhood at multiple scales. We explore two ranking
methods that use this representation. The first uses a dynamic programming
time-warping algorithm to align two representations, and interprets the
alignment cost as a measure of similarity. This algorithm also exploits learned
spatial weights to downweight matches from regions of unstable curvature. The
second interprets the representation as a feature descriptor. Feature keypoints
are defined at the local extrema of the representation. Descriptors for the set
of known individuals are stored in a tree structure, which allows us to perform
queries given the descriptors from an unknown trailing edge. We evaluate the
top-k accuracy on two real-world datasets to demonstrate the effectiveness of
the curvature representation, achieving top-1 accuracy scores of approximately
95% and 80% for bottlenose dolphins and humpback whales, respectively.

通过积分曲率等方法，识别鲸鱼和海豚的尾鳍轮廓，较高准确度地区分个体。

海豚和鲸鱼识别的曲率表示和匹配算法

Integral Curvature Representation and Matching Algorithms for  Identification of Dolphins and Whales

Class ambiguity is typical in image classification problems with a large
number of classes. When classes are difficult to discriminate, it makes sense
to allow k guesses and evaluate classifiers based on the top-k error instead of
the standard zero-one loss. We propose top-k multiclass SVM as a direct method
to optimize for top-k performance. Our generalization of the well-known
multiclass SVM is based on a tight convex upper bound of the top-k error. We
propose a fast optimization scheme based on an efficient projection onto the
top-k simplex, which is of its own interest. Experiments on five datasets show
consistent improvements in top-k accuracy compared to various baselines.

提出一种直接优化 top-k 性能的方法 ——top-k 多类支持向量机，其基于一种严格的上凸界并提出一种基于 top-k 单纯形的高效投影优化方案，在五个数据集上在 top-k 精度方面相比各种基线模型均有不同程度的提高。