Classification of environmental microorganisms in microscopic images using shape features and support vector machines

Li, Chen; Shirahama, Kimiaki; Grzegorzek, Marcin; Ma, Fangshu; Zhou, Beihai

doi:10.1109/icip.2013.6738502

Cited by 21 publications

(13 citation statements)

References 9 publications

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“…A classification result represents the ranking of test images based on scores in Eq. (12). Finally, we use Average Precision (AP) as the evaluation measure of a classification result, and the Mean of APs (MAP) for all 15 classes as the overall measure.…”

Section: Resultsmentioning

confidence: 99%

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Environmental Microorganism Classification Using Sparse Coding and Weakly Supervised Learning

Shirahama

Grzegorzek

2015

Proceedings of the 2nd International Workshop on Environmental Multimedia Retrieval

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Environmental Microorganisms (EMs), such as Epistylis and Rotifera, are very tiny living beings in human environments and decompose pollutants as their nutrition. The classification of EMs plays a fundamental role for establishing sustainable ecosystem. However, this is traditionally done by molecular methods using DNA or RNA analysis, or morphological methods using microscopes. These methods require huge monetary costs or manual efforts. In this paper, we propose an EM classification method by directly processing microscopic images. We especially address two problems: The first is a small training dataset problem. Because EM samples are collected from unstable real-world environments, it is difficult to stably obtain a large number of EM images. To effectively represent scarce training images, we use Sparse Coding (SC) which extracts sufficient local features from an image and reconstructs it by a sparse linear combination of bases. The second is a noisy image problem. Because EM samples are often found in dirty regions, their images often show a noisy background. To overcome this, we use Weakly Supervised Learning (WSL) which jointly performs the localisation and classification of EMs by examining the local information in training images. Experimental results on real-world images show the effectiveness and potential of our system.

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Section: Resultsmentioning

confidence: 99%

“…EMs are investigated in [11,23,10,12]. The researchers first conduct semi-automatic image segmentation to extract the region of an EM in each image.…”

Section: Related Workmentioning

confidence: 99%

Environmental Microorganism Classification Using Sparse Coding and Weakly Supervised Learning

Shirahama

Grzegorzek

2015

Proceedings of the 2nd International Workshop on Environmental Multimedia Retrieval

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“…In additiona,SVM is also used to classify Australian native forest species [61] and classify the environmental microorganisms [62] as well as discovery and design new drugs [63].…”

Section: B Biological Information Processingmentioning

confidence: 99%

Introduction of SVM algorithms and recent applications about fault diagnosis and other aspects

Yin

Liu

Krueger

et al. 2015

2015 IEEE 13th International Conference on Industrial Informatics (INDIN)

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Support vector machine has obtained more and more attentions as a new method of machine learning based on the statistic learning theory.At the same time,there are increasing concerns about the fault diagnosis for practical engineering systems.Firstly,many kinds of SVM algorithms will be introduced,such as LS-SVM,LSVM and PSVM and so on.Besides,the advantages and disadvantage of those methods will be introduced.Finally,we discuss fault diagnosis and other aspects' recent applications and the directions we should research in the future.

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“…In Li et al (2013b), different shape features are compared in an EM classification task, where a multi-class SVM classifier is applied. In the experiment, ten classes of EMs are used to test the effectiveness of different shape features.…”

Section: Overview Of Em Classificationmentioning

confidence: 99%

A survey for the applications of content-based microscopic image analysis in microorganism classification domains

Wang

2017

Artif Intell Rev

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Microorganisms such as protozoa and bacteria play very important roles in many practical domains, like agriculture, industry and medicine. To explore functions of different categories of microorganisms is a fundamental work in biological studies, which can assist biologists and related scientists to get to know more properties, habits and characteristics of these tiny but obbligato living beings. However, taxonomy of microorganisms (microorganism classification) is traditionally investigated through morphological, chemical or physical analysis, which is time and money consuming. In order to overcome this, since the 1970s innovative content-based microscopic image analysis (CBMIA) approaches are introduced to microbiological fields. CBMIA methods classify microorganisms into different categories using multiple artificial intelligence approaches, such as machine vision, pattern recognition and machine learning algorithms. Furthermore, because CBMIA approaches are semior full-automatic computer-based methods, they are very efficient and labour cost saving, supporting a technical feasibility for microorganism classification in our current big data age. In this article, we review the development history of microorganism classification using CBMIA approaches with two crossed pipelines. In the first pipeline, all related works are grouped by their corresponding microorganism application domains. By this pipeline, it is easy for microbiologists to have an insight into each special application domain and find their interested applied CBMIA techniques. In the second pipeline, the related works in each application domain are reviewed by time periods. Using this pipeline, computer scientists can see the dynamic of technological development clearly and keep up with the future devel-

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Classification of environmental microorganisms in microscopic images using shape features and support vector machines

Cited by 21 publications

References 9 publications

Environmental Microorganism Classification Using Sparse Coding and Weakly Supervised Learning

Environmental Microorganism Classification Using Sparse Coding and Weakly Supervised Learning

Introduction of SVM algorithms and recent applications about fault diagnosis and other aspects

A survey for the applications of content-based microscopic image analysis in microorganism classification domains

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