Review of the application of machine learning to the automatic semantic annotation of images

Olaode, Abass A.; Naghdy, Golshah

doi:10.1049/iet-ipr.2018.6153

Cited by 6 publications

(5 citation statements)

References 137 publications

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“…Technologies such as sensor networks, sensor web, and semantic sensor networks process IoT data at different granularities and attempt to address interoperability issues in the IoT [7]. Sensor networks connect various sensors to the network through proximity wireless communication technologies and then monitor and record environmental conditions in each area, including aspects such as temperature and humidity [8].…”

Section: Related Workmentioning

confidence: 99%

Semantic Association and Decision-Making for the Internet of Things Based on Partial Differential Fuzzy Unsupervised Models

Liu¹,

Lu²

2022

Mathematical Problems in Engineering

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This study presents an in-depth study and analysis of IoT semantic association and decision-making using a partial differential fuzzy unsupervised approach. It focuses on a semantic annotation framework for device metadata and a knowledge base construction method to further improve the interoperability of IoT domain knowledge by building a unified IoT domain knowledge base and designing and implementing a semantic IoT knowledge management and application generation system. The main proposal is an IoT generic domain ontology, which reuses the existing excellent ontologies of IoT as much as possible, extracts the commonly used concepts of the domain and combines them, and provides a unified semantic template for IoT applications. On the other hand, by applying the entity linking technique to the extension of the knowledge base and linking the structured metadata of devices to the corresponding entities of the background knowledge base, the domain knowledge base can be made to share the rich background knowledge. At the same time, the interoperability of heterogeneous IoT metadata between applications is enhanced by unifying data and concepts from different device applications to the same background knowledge base through entity alignment techniques. The semantic representation of events applicable to IoT application scenarios is investigated, and an IoT event ontology for representing abstract events and event relationships in IoT is designed; next, a domain ontology with IoT sensing and control event representation capability is constructed based on the IoT event ontology, in which the typical domain ontology (SSN) that can be used for IoT applications is followed by the ontology reuse principle is improved and extended to support the description of event types and interevent relationships, and the IoT event model is associated with the improved IoT base ontology through an ontology alignment approach. Finally, the IoT sensing and control ontology are validated by semantic modeling of device composition, component relationships, and operational processes based on the IoT sensing and control ontology.

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Section: Related Workmentioning

confidence: 99%

Semantic Association and Decision-Making for the Internet of Things Based on Partial Differential Fuzzy Unsupervised Models

Liu¹,

Lu²

2022

Mathematical Problems in Engineering

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“…A critical aspect of leveraging remote sensing data for vegetation mapping is the choice of classification approach, which largely dictates the accuracy and reliability of the resulting maps. Among the various classification approaches, unsupervised classification has emerged as a vital technique in minimizing researcher subjectivity at all stages of vegetation mapping, from developing a classification scheme to assigning a particular geographic point to a specific ecosystem type [12][13][14][15][16]. Unlike supervised classification, which relies on prior knowledge and extensive field samples, unsupervised classification operates without any a priori knowledge, solely depending on spectrally pixel-based statistics [13].…”

Section: Introductionmentioning

confidence: 99%

Forest Community Spatial Modeling Using Machine Learning and Remote Sensing Data

Gafurov,

Prokhorov,

Kozhevnikova

et al. 2024

Remote Sensing

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This study examines the application of unsupervised classification techniques in the mapping of forest vegetation, aiming to align vegetation cover with the Braun-Blanquet classification system through remote sensing. By leveraging Landsat 8 and 9 satellite imagery and advanced clustering algorithms, specifically the Weka X-Means, this research addresses the challenge of minimizing researcher subjectivity in vegetation mapping. The methodology incorporates a two-step clustering approach to accurately classify forest communities, utilizing a comprehensive set of vegetation indices to distinguish between different types of forest ecosystems. The validation of the classification model relied on a detailed analysis of over 17,000 relevés from the “Flora” database, ensuring a high degree of accuracy in matching satellite-derived vegetation classes with field observations. The study’s findings reveal the successful identification of 44 forest community types that was aggregated into seven classes of Braun-Blanquet classification system, demonstrating the efficacy of unsupervised classification in generating reliable vegetation maps. This work not only contributes to the advancement of remote sensing applications in ecological research, but also provides a valuable tool for natural resource management and conservation planning. The integration of unsupervised classification with the Braun-Blanquet system presents a novel approach to vegetation mapping, offering insights into ecological characteristics, and can be good starter point for sequestration potential of forest communities’ assessment in the Republic of Tatarstan.

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“…As a remedy, various machine learning techniques were proposed to produce "bronze standard" labeled datasets in an unsupervised or semi-supervised manner, minimizing the human involvement [15]. Probabilistic generative models built through topic modeling were utilized to establish semantic membership and assign labels to images [18], social media streaming data [19,20], or textual documents [21]. While being theoretically straightforward, topic model-driven annotation is prone to errors when annotated data include multiple topic-specific segments [22].…”

Section: Introduction and Literature Survey 1backgroundmentioning

confidence: 99%

A Text Segmentation Approach for Automated Annotation of Online Customer Reviews, Based on Topic Modeling

2022

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Online customer review classification and analysis have been recognized as an important problem in many domains, such as business intelligence, marketing, and e-governance. To solve this problem, a variety of machine learning methods was developed in the past decade. Existing methods, however, either rely on human labeling or have high computing cost, or both. This makes them a poor fit to deal with dynamic and ever-growing collections of short but semantically noisy texts of customer reviews. In the present study, the problem of multi-topic online review clustering is addressed by generating high quality bronze-standard labeled sets for training efficient classifier models. A novel unsupervised algorithm is developed to break reviews into sequential semantically homogeneous segments. Segment data is then used to fine-tune a Latent Dirichlet Allocation (LDA) model obtained for the reviews, and to classify them along categories detected through topic modeling. After testing the segmentation algorithm on a benchmark text collection, it was successfully applied in a case study of tourism review classification. In all experiments conducted, the proposed approach produced results similar to or better than baseline methods. The paper critically discusses the main findings and paves ways for future work.

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Review of the application of machine learning to the automatic semantic annotation of images

Cited by 6 publications

References 137 publications

Semantic Association and Decision-Making for the Internet of Things Based on Partial Differential Fuzzy Unsupervised Models

Semantic Association and Decision-Making for the Internet of Things Based on Partial Differential Fuzzy Unsupervised Models

Forest Community Spatial Modeling Using Machine Learning and Remote Sensing Data

A Text Segmentation Approach for Automated Annotation of Online Customer Reviews, Based on Topic Modeling

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