Classification and retrieval on macroinvertebrate image databases

Kıranyaz, Serkan; İnce, Türker; Pulkkinen, Jenni; Gabbouj, Moncef; írje, Johanna; Kärkkäinen, Salme; Tirronen, Ville; Juhola, Martti; Turpeinen, Tuomas; Meissner, Kristian

doi:10.1016/j.compbiomed.2011.04.008

Cited by 42 publications

(54 citation statements)

References 26 publications

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“…The observed levels of taxonomical accuracy match levels of human accuracy for other aquatic taxonomic groups [8]. In follow-up work on the same dataset [9], a significant improvement on the classification accuracy was achieved. The Following an evolutionary radial basis function (RBF) neural network approach the classification error (CE) rate was 7.41% for train and 5.14% for the test set.…”

Section: Introductionmentioning

confidence: 58%

“…In order to perform comparative evaluations, the same Benthic macroinvertebrate image database as in [7] and [9] is used in this work. This database consists of 1350 images representing 8 different taxonomical groups: Baetis rhodani, Diura nanseni, Heptagenia sulphurea, Hydropsyche pellucidulla, Hydropsyche siltalai, Isoperla sp., Rhyacophila nubila and Taeniopteryx nebulosa.…”

Section: Dataset Creation and Feature Extractionmentioning

confidence: 99%

“…Although these preliminary results are promising, their practical use is limited to fixed (static) datasets with low number of classes, features and dimensions like in [8] and [9], where only 8 classes were used. Using a single classifier invokes the problem of scalability, that is, in the database both the number of feature space dimensions and classes cannot exceed certain limits without rendering the proper training of the classifier(s) infeasible due to the massive size of the input and output layers.…”

Section: Introductionmentioning

confidence: 99%

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Network of evolutionary binary classifiers for classification and retrieval in macroinvertebrate databases

Kıranyaz

Gabbouj

Pulkkinen

et al. 2010

2010 IEEE International Conference on Image Processing

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In this paper, we focus on advanced classification and data retrieval schemes that are instrumental when processing large taxonomical image datasets. With large number of classes, classification and an efficient retrieval of a particular benthic macroinvertebrate image within a dataset will surely pose a severe problem. To address this, we propose a novel network of evolutionary binary classifiers, which is scalable, dynamically adaptable and highly accurate for the classification and retrieval of large biological species-image datasets. The classification and retrieval results for the macroinvertebrate test data attain taxonomic accuracy that equals and even surpasses that of an average expert. Our findings are encouraging for aquatic biomonitoring where cost intensity of sample analysis currently poses a bottleneck for routine biomonitoring.

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Section: Introductionmentioning

confidence: 58%

Section: Dataset Creation and Feature Extractionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Network of evolutionary binary classifiers for classification and retrieval in macroinvertebrate databases

Kıranyaz

Gabbouj

Pulkkinen

et al. 2010

2010 IEEE International Conference on Image Processing

Self Cite

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“…Crossed work Besides the related work above, the research of EM classification (Akiba and Kahui 2000;Amaral 2003;Amaral et al 1999Amaral et al , 2004Amaral et al , 2008Chen and Li 2008;Ferreira and Rasband 2012;Ginoris et al 2006Ginoris et al , 2007aGray et al 2002;Jenne et al 2001Jenne et al , 2002Jenne et al , 2003Kiranyaz et al 2011;Chen 2008, 2009;Motta et al 2001;Ruusuvuori et al 2008;Thiel and Davies 1995;Thiel andWiltshire 1995 in Sect. 3 andMM classification (Javidi et al 2006g) in Sect.…”

Section: Original Methodsmentioning

confidence: 99%

“…In Kiranyaz et al (2011), a CBMIA system is proposed to classify micro-alike images of macroinvertebrate, where multiple global, local, shape, colour, pair-wise, texture features are extracted using a third party toolbox (ImageJ Ferreira and Rasband 2012), and then SVM, Bayesian and two ANN classifiers are designed. In the experiment, 1350 micro-alike images of eight different texonomical groups are used for system evaluation, and finally the lowest classification error rate around 6% is achieved by the SVM classifier.…”

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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Breaking the curse of dimensionality in quadratic discriminant analysis models with a novel variant of a Bayes classifier enhances automated taxa identification of freshwater macroinvertebrates

et al. 2013

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Macroinvertebrate samples are commonly used in biomonitoring to study changes on aquatic ecosystems. Traditionally, specimens are identified manually to taxa by human experts being time‐consuming and cost intensive. Using the image data of 35 taxa and 64 features, we propose a novel variant of the quadratic discriminant analysis for breaking the curse of dimensionality in quadratic discriminant analysis models. Our variant, called a random Bayes array (RBA), uses bagging and random feature selection similar to random forest. We explore several variations of RBA. We consider three classification (i.e taxa identification) decisions: majority vote, averaged posterior probabilities, and a novel approach; a score of weighted votes. Besides modifying the voting, we propose to weight features according to their importance instead of eliminating the least important features. We compared the performance of RBA with traditional Bayesian and several other popular classification methods and assessed how the methods behave in relation to each other and the different macroinvertebrate species. Further, we investigate how severely misclassifications affect the performance of different methods when set into a biomonitoring context. We found that the lowest and least severe classification error (i.e. most accurate taxa identification) was achieved with RBA by using averaged posterior probabilities and weighted features. Copyright © 2013 John Wiley & Sons, Ltd.

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Classification and retrieval on macroinvertebrate image databases

Cited by 42 publications

References 26 publications

Network of evolutionary binary classifiers for classification and retrieval in macroinvertebrate databases

Network of evolutionary binary classifiers for classification and retrieval in macroinvertebrate databases

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

Breaking the curse of dimensionality in quadratic discriminant analysis models with a novel variant of a Bayes classifier enhances automated taxa identification of freshwater macroinvertebrates

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