Automatic plankton quantification using deep features
Automatic plankton quantification using deep features
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DOI：
10.1093/plankt/fbz023
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发表时间：
2019-07-01
影响因子：
2.1
通讯作者：
Sosik, Heidi M.
中科院分区：
文献类型：
--
作者：
Gonzalez, Pablo;Castano, Alberto;Sosik, Heidi M.
海洋浮游生物数据的研究对于监测世界海洋的健康至关重要。近几十年来，自动浮游生物识别系统已被证明对处理由专门设计的原位数字成像系统收集的大量数据很有用。一开始，这些系统是使用传统的自动分类技术开发并投入运行的，这些自动分类技术被人工设计的局部图像描述符（如傅里叶特征）所填充，获得了相当成功的结果。在过去的几年里，随着神经网络的重生，计算机视觉领域取得了许多进展。在本文中，我们利用用域外数据训练的卷积神经网络计算的描述符，在估计水样中每种浮游生物类别的流行率的任务中，取代手工设计的描述符。为了实现这一目标，我们设计了一套广泛的实验，以展示这些深度特征与最先进的量化算法结合使用时的有效性。
The study of marine plankton data is vital to monitor the health of the world's oceans. In recent decades, automatic plankton recognition systems have proved useful to address the vast amount of data collected by specially engineered in situ digital imaging systems. At the beginning, these systems were developed and put into operation using traditional automatic classification techniques, which were fed with hand-designed local image descriptors (such as Fourier features), obtaining quite successful results. In the past few years, there have been many advances in the computer vision community with the rebirth of neural networks. In this paper, we leverage how descriptors computed using convolutional neural networks trained with out-of-domain data are useful to replace hand-designed descriptors in the task of estimating the prevalence of each plankton class in a water sample. To achieve this goal, we have designed a broad set of experiments that show how effective these deep features are when working in combination with state-of-the-art quantification algorithms.