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Hierarchical classification with reject option for live fish recognition

机译：带有拒绝选项的层次分类，用于活鱼识别

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摘要

A live fish recognition system is needed in application scenarios where manual annotation is too expensive, i.e. too many underwater videos. We present a novel balance-enforced optimized tree with reject option (BEOTR) for live fish recognition. It recognizes the top 15 common species of fish and detects new species in an unrestricted natural environment recorded by underwater cameras. The three main contributions of the paper are: (1) a novel hierarchical classification method suited for greatly unbalanced classes, (2) a novel classification-rejection method to clear up decisions and reject unknown classes, (3) an application of the classification method to free swimming fish. This system assists ecological surveillance research, e.g. fish population statistics in the open sea. BEOTR is automatically constructed based on inter-class similarities. Afterwards, trajectory voting is used to eliminate accumulated errors during hierarchical classification and, therefore, achieves better performance. We apply a Gaussian mixture model and Bayes rule as a reject option after the hierarchical classification to evaluate the posterior probability of being a certain species to filter less confident decisions. The proposed BEOTR-based hierarchical classification method achieves significant improvements compared to state-of-the-art techniques on a live fish image dataset of 24,150 manually labelled images from South Taiwan Sea.
机译：在人工注释过于昂贵（即，水下视频过多）的应用场景中，需要使用活鱼识别系统。我们提出了带有拒绝选项（BEOTR）的新颖平衡增强优化树，用于活鱼识别。它可以识别最常见的15种鱼类，并在水下相机记录的不受限制的自然环境中检测到新的鱼类。本文的三个主要贡献是：（1）一种适用于严重不平衡类的新颖的分层分类方法；（2）一种新颖的分类拒绝方法以清除决策并拒绝未知类；（3）该分类方法的应用自由游泳的鱼。该系统有助于生态监测研究，例如公海鱼类种群统计。 BEOTR是基于类间相似性自动构建的。之后，使用轨迹投票来消除层次分类过程中累积的错误，因此可以获得更好的性能。在分层分类之后，我们将高斯混合模型和贝叶斯规则作为拒绝选项，以评估成为某种物种以过滤不太自信的决策的后验概率。与最新技术相比，基于BEOTR的分层分类方法在来自南台湾海的24,150个手动标记图像的活鱼图像数据集上实现了重大改进。

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