Combining clustering and active learning for the detection and learning of new image classes

Coletta, Luiz Fernando Sommaggio; Ponti, Moacir Antonelli; Hruschka, Eduardo R.; Acharya, Ayan; Ghosh, Joydeep

doi:10.1016/j.neucom.2019.04.070

Cited by 28 publications

(12 citation statements)

References 32 publications

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“…In Coletta et al. ( 2019 ), a meaningful image detector to detect new classes that is not labelled is designed. The proposed detector is an iterative one by combining SVM and clustering algorithms.…”

Section: Methodology Analysismentioning

confidence: 99%

A state-of-the-art survey of object detection techniques in microorganism image analysis: from classical methods to deep learning approaches

Rahaman

et al. 2022

Artif Intell Rev

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Microorganisms play a vital role in human life. Therefore, microorganism detection is of great significance to human beings. However, the traditional manual microscopic detection methods have the disadvantages of long detection cycle, low detection accuracy in large orders, and great difficulty in detecting uncommon microorganisms. Therefore, it is meaningful to apply computer image analysis technology to the field of microorganism detection. Computer image analysis can realize high-precision and high-efficiency detection of microorganisms. In this review, first,we analyse the existing microorganism detection methods in chronological order, from traditional image processing and traditional machine learning to deep learning methods. Then, we analyze and summarize these existing methods and introduce some potential methods, including visual transformers. In the end, the future development direction and challenges of microorganism detection are discussed. In general, we have summarized 142 related technical papers from 1985 to the present. This review will help researchers have a more comprehensive understanding of the development process, research status, and future trends in the field of microorganism detection and provide a reference for researchers in other fields.

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Section: Methodology Analysismentioning

confidence: 99%

A state-of-the-art survey of object detection techniques in microorganism image analysis: from classical methods to deep learning approaches

Rahaman

et al. 2022

Artif Intell Rev

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“…In [192], a meaningful image detector to detect new classes that is not labelled is designed. The proposed detector is an iterative one by combining SVM and clustering algorithms.…”

Section: Other Potential Methodsmentioning

confidence: 99%

“…Improving accuracy 2019 [192] An iterative detector Detecting new classes 2020 [195] DR-CNN Detecting small object in a large scene 2020 [54] YOLOv4 Improving precision and efficiency 2021 [198] FRCNN-GNB Improving accuracy…”

Section: Yolov3mentioning

confidence: 99%

A State-of-the-art Survey of Object Detection Techniques in Microorganism Image Analysis: From Classical Methods to Deep Learning Approaches

Ma¹,

Li²,

Rahaman³

et al. 2021

Preprint

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Microorganisms play a vital role in human life. Therefore, microorganism detection is of great significance to human beings. However, the traditional manual microscopic detection methods have the disadvantages of long detection cycle, low detection accuracy in large orders, and great difficulty in detecting uncommon microorganisms. Therefore, it is meaningful to apply computer image analysis technology to the field of microorganism detection. Computer image analysis can realize high-precision and high-efficiency detection of microorganisms. In this review, first,we analyse the existing microorganism detection methods in chronological order, from traditional image processing and traditional machine learning to deep learning methods. Then, we analyze and summarize these existing methods and introduce some potential methods, including visual transformers. In the end, the future development direction and challenges of microorganism detection are discussed. In general, we have summarized 137 related technical papers from 1985 to the present. This review will help researchers have a more comprehensive understanding of the development process, research status, and future trends in the field of microorganism detection and provide a reference for researchers in other fields.

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“…In [11] authors combine classification and clusterization to obtain methods for detecting new classes of images that did not appear in the dataset during training. They transfer the knowledge of a previously trained classifier by improving the C3E algorithm [1] which works under the assumption that similar instances found by clustering algorithms are more likely to share the same class label obtained with the classifier.…”

Section: Knowledge Enriched Clusteringmentioning

confidence: 99%

KnAC: an approach for enhancing cluster analysis with background knowledge and explanations

Bobek¹,

Kuk²,

Brzegowski³

et al. 2021

Preprint

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Pattern discovery in multidimensional data sets has been a subject of research since decades. There exists a wide spectrum of clustering algorithms that can be used for that purpose. However, their practical applications share in common the post-clustering phase, which concerns expert-based interpretation and analysis of the obtained results. We argue that this can be a bottleneck of the process, especially in the cases where domain knowledge exists prior to clustering. Such a situation requires not only a proper analysis of automatically discovered clusters, but also a conformance checking with existing knowledge. In this work, we present Knowledge Augmented Clustering (KNAC), which main goal is to confront expert-based labelling with automated clustering for the sake of updating and refining the former. Our solution does not depend on any ready clustering algorithm, nor introduce one. Instead KNAC can serve as an augmentation of an arbitrary clustering algorithm, making the approach robust and model-agnostic. We demonstrate the feasibility of our method on artificially, reproducible examples and on a real life use case scenario.

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Combining clustering and active learning for the detection and learning of new image classes

Cited by 28 publications

References 32 publications

A state-of-the-art survey of object detection techniques in microorganism image analysis: from classical methods to deep learning approaches

A state-of-the-art survey of object detection techniques in microorganism image analysis: from classical methods to deep learning approaches

A State-of-the-art Survey of Object Detection Techniques in Microorganism Image Analysis: From Classical Methods to Deep Learning Approaches

KnAC: an approach for enhancing cluster analysis with background knowledge and explanations

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