Automatic image annotation method based on a convolutional neural network with threshold optimization

Cao, Jianfang; Zhao, Aidi; Zhang, Zibang

doi:10.1371/journal.pone.0238956

Cited by 17 publications

(9 citation statements)

References 30 publications

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“…Semiautomated labeling tasks [25,26] for large-scale datasets in different domains are increasingly gaining attention, with potential to nudge humans and assume the supervision role of filtering, selecting, and updating to ease the tedious (and repetitive) labeling burden. Moreover, automated labeling tasks [27,28] hold potential to eliminate human-in-the-loop based on some predefined model of classes. Zhou et al [27] affirm that fully automated labeling has the propensity to yield better results than manual image labels, as the latter is always subject to human bias and error.…”

Section: Choosing Appropriate Label and Ground Truth Definitionmentioning

confidence: 99%

Data Readiness and Data Exploration for Successful Power Line Inspection

Antwi-Bekoe,

Tietaa Maale,

Mensah Martey

et al. 2024

Artificial Intelligence

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Sufficiently large, curated, and representative training data remains key to successful implementation of deep learning applications for wide-scale power line inspection. However, most researchers have offered limited insight regarding the inherent readiness of the knowledge bases that drives power line algorithm development. In most cases, these high dimensional datasets are also unexplored before modeling. In this article, power line image data readiness (PLIDaR) scale for AI algorithm development is proposed. Using the PLIDaR benchmark, this study analyzes the fundamental steps involved in preparing overhead transmission power line (OTPL) insulator image data for deep supervised learning algorithm development. Data visualization approach is implemented by reengineering the ground truth instance annotations of two recent public insulator datasets, while exploratory data analysis is also employed by implementing a robust dimensionality reduction technique to optimize construction, visualization, clustering, and analysis of these recent insulator datasets in a lower dimensional space. The implementations reveal representational variabilities and hidden patterns that could be exploited to improve data quality before predictive modeling. Moreover, the visualizations from dimensionality reduction technique have potential to help develop classifiers that are more reliable.

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Section: Choosing Appropriate Label and Ground Truth Definitionmentioning

confidence: 99%

Data Readiness and Data Exploration for Successful Power Line Inspection

Antwi-Bekoe,

Tietaa Maale,

Mensah Martey

et al. 2024

Artificial Intelligence

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“…However, it is an even higher time-consuming task for large databases, therefore automatic annotation procedures are being developed. Thus, other conventional machine learning methods and deep learning procedures are used to automatically annotate images ( Murthy et al, 2015 ; Cao et al, 2020 ).…”

Section: Novel Diagnostic Tools By Using Image Analysis Techniquesmentioning

confidence: 99%

Advances and challenges in automated malaria diagnosis using digital microscopy imaging with artificial intelligence tools: A review

Maturana

Oliveira²,

Nadal³

et al. 2022

Front. Microbiol.

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Malaria is an infectious disease caused by parasites of the genus Plasmodium spp. It is transmitted to humans by the bite of an infected female Anopheles mosquito. It is the most common disease in resource-poor settings, with 241 million malaria cases reported in 2020 according to the World Health Organization. Optical microscopy examination of blood smears is the gold standard technique for malaria diagnosis; however, it is a time-consuming method and a well-trained microscopist is needed to perform the microbiological diagnosis. New techniques based on digital imaging analysis by deep learning and artificial intelligence methods are a challenging alternative tool for the diagnosis of infectious diseases. In particular, systems based on Convolutional Neural Networks for image detection of the malaria parasites emulate the microscopy visualization of an expert. Microscope automation provides a fast and low-cost diagnosis, requiring less supervision. Smartphones are a suitable option for microscopic diagnosis, allowing image capture and software identification of parasites. In addition, image analysis techniques could be a fast and optimal solution for the diagnosis of malaria, tuberculosis, or Neglected Tropical Diseases in endemic areas with low resources. The implementation of automated diagnosis by using smartphone applications and new digital imaging technologies in low-income areas is a challenge to achieve. Moreover, automating the movement of the microscope slide and image autofocusing of the samples by hardware implementation would systemize the procedure. These new diagnostic tools would join the global effort to fight against pandemic malaria and other infectious and poverty-related diseases.

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“…In the realm of remote sensing, it is important to annotate scene images with multiple labels in order to comprehend the images [12,13]. Qi et al (2020) constructed a multi-label high spatial resolution dataset to understand well about semantic scene images with deep learning approach from the overhead perspective.…”

Section: State Of the Artmentioning

confidence: 99%

Semantic Annotation of Land Cover Remote Sensing Images Using Fuzzy CNN

Saranya¹,

Bhuvaneswari²

2022

Intelligent Automation &Amp; Soft Computing

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This paper presents a novel fuzzy logic based Convolution Neural Network intelligent classifier for accurate image classification. The proposed approach employs a semantic class label model that classifies the input land cover images into a set of semantic categories and classes depending on the content. The intelligent feature selection algorithm selects the prominent attributes from the given data set using weighted attribute functions and uses fuzzy logic to build the rules based on the membership values. To annotate remote sensing images, the CNN method effectively creates semantics and categorises images. The decision manager then integrates the fuzzy logic rules with the CNN algorithm to achieve accurate classification. The proposed approach achieves a classification accuracy of 90.46% when used with various training and test images, and the three class labels for vegetation (84%), buildings (90%), and roads (90%) provide a higher classification accuracy than other existing algorithms. On the basis of true positive rate, false positive rate, and accuracy of picture classification, the suggested approach outperforms the existing methods.

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Automatic image annotation method based on a convolutional neural network with threshold optimization

Cited by 17 publications

References 30 publications

Data Readiness and Data Exploration for Successful Power Line Inspection

Data Readiness and Data Exploration for Successful Power Line Inspection

Advances and challenges in automated malaria diagnosis using digital microscopy imaging with artificial intelligence tools: A review

Semantic Annotation of Land Cover Remote Sensing Images Using Fuzzy CNN

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