A Hybrid Convolutional Neural Network for Plankton Classification

Dai, Jialun; Yu, Zhibin; Zheng, Haiyong; Zheng, Bing; Wang, Nan

doi:10.1007/978-3-319-54526-4_8

Cited by 28 publications

(25 citation statements)

References 23 publications

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“…Some of the recent work presented in last few years on the project of automatic classification of marine zooplankton is summarized in Table I and discussed in later section. Author in [1] proposed another hybrid CNN model for plankton classification which consists of 3 AlexNet networks and fuses together at final fully connected layer. The threechannel pyramid structured network, which takes original image and two preprocessed copies of it as input respectively is trained over WHOI-Plankton dataset containing 30000 images of 30 classes.…”

Section: Related Workmentioning

confidence: 99%

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Classification of Freshwater Zooplankton by Pre-trained Convolutional Neural Network in Underwater Microscopy

Hong¹,

Mehdi²,

Hui³

et al. 2020

IJACSA

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Zooplankton is enormously diverse and fundamental group of microorganisms that exists in almost every freshwater body, determining its ecology and play a vital role in food chain. Considering the significance of zooplankton, the study of freshwater zooplankton is very essential which intensely relies on the classification of images. However, the routine manual analysis and classification is laborious, time consuming and expensive, and poses a significant challenge to experts. Thus, for recent decade much research is focused on the development of underwater imaging technologies and intelligent classification system of zooplankton. This work presents devotion to observation of freshwater zooplankton by designed underwater microscope and modeling the system for automatic classification among four different taxa. Unlike most of the existing zooplankton image classification systems, this model is trained on a comparatively small dataset collected from freshwater by designed underwater microscope. Transfer learning of pretrained AlexNet Convolutional Neural Network (CNN) model proved to be a potential approach in the system design. Among four networks trained over two datasets, the best overall classification accuracy of up to 93.1%, comparable to other existing systems was achieved on test dataset (92.5% for Calanoid and Cyclopoid (Female), 90% for Cyclopoid (Male) and 97.5% for Daphnia). Graphical User Interface (GUI) of the model constructed on MATLAB, makes it easy for the users to collect images for building database, train network and to classify images of different taxa. Moreover, the designed system is adaptable to the addition of more classes in the future.

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Section: Related Workmentioning

confidence: 99%

“…Zooplankton belongs to the class of microorganisms, also known as "drifters", can be found in loads, suspended in freshwater bodies and other huge aquatic ecosystems [1]. Freshwater zooplankton community is diverse (>20 types), and occur in almost every lake, with the body size ranging from few tens of microns to >2mm.…”

Section: Introductionmentioning

confidence: 99%

Classification of Freshwater Zooplankton by Pre-trained Convolutional Neural Network in Underwater Microscopy

Hong¹,

Mehdi²,

Hui³

et al. 2020

IJACSA

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show abstract

“…Dai et al (2016a) present a similar approach in the design of a zooplankton classifier. Other authors combine CNNs with different machine learning techniques, including active learning (Bochinski et al, 2018), hybrid systems (Dai et al, 2016b), parallel networks (Wang et al, 2018), imbalance learning (Lee et al, 2016) or different forms of information fusion (Cui et al, 2018;Lumini and Nanni, 2019). It should be noted that the improvements on plankton classification described in these papers may not be directly transferable to quantification systems, as classification and quantification are two different tasks.…”

Section: Introductionmentioning

confidence: 99%

Automatic plankton quantification using deep features

González

Castaño

Peacock

et al. 2019

Journal of Plankton Research

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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.

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“…The results were poor with a maximum performance of 73.90%. In [23], a database of 30,000 images belonging to 33 classes was used. They obtained an accuracy up to 96.3%.…”

Section: Introductionmentioning

confidence: 99%

Automated Diatom Classification (Part A): Handcrafted Feature Approaches

et al. 2017

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This paper deals with automatic taxa identification based on machine learning methods. The aim is therefore to automatically classify diatoms, in terms of pattern recognition terminology. Diatoms are a kind of algae microorganism with high biodiversity at the species level, which are useful for water quality assessment. The most relevant features for diatom description and classification have been selected using an extensive dataset of 80 taxa with a minimum of 100 samples/taxon augmented to 300 samples/taxon. In addition to published morphological, statistical and textural descriptors, a new textural descriptor, Local Binary Patterns (LBP), to characterize the diatom's valves, and a log Gabor implementation not tested before for this purpose are introduced in this paper. Results show an overall accuracy of 98.11% using bagging decision trees and combinations of descriptors. Finally, some phycological features of diatoms that are still difficult to integrate in computer systems are discussed for future work.

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A Hybrid Convolutional Neural Network for Plankton Classification

Cited by 28 publications

References 23 publications

Classification of Freshwater Zooplankton by Pre-trained Convolutional Neural Network in Underwater Microscopy

Classification of Freshwater Zooplankton by Pre-trained Convolutional Neural Network in Underwater Microscopy

Automatic plankton quantification using deep features

Automated Diatom Classification (Part A): Handcrafted Feature Approaches

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