Online Learning of Weighted Relational Rules for Complex Event Recognition

Katzouris, Nikos; Michelioudakis, Evangelos; Artikis, Alexander; Παλιούρας, Γεώργιος

doi:10.1007/978-3-030-10928-8_24

Cited by 10 publications

(9 citation statements)

References 25 publications

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“…On the other hand, OSLα is based on MLNs and thus inherits their probabilistic properties, but its structure learning component is sub-optimal, i.e., it tends to generate large sets of clauses, many of which have low heuristic value. An in-depth comparison of these systems can be found in (Katzouris et al, 2018). More importantly, both OSLα and OLED are supervised learners and in the presence of unlabelled training examples they impose closed-world assumption, that is, they assume everything not known is false, i.e., negative examples.…”

Section: Event Calculus and Structure Learningmentioning

confidence: 99%

Semi-supervised online structure learning for composite event recognition

2019

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Online structure learning approaches, such as those stemming from Statistical Relational Learning, enable the discovery of complex relations in noisy data streams. However, these methods assume the existence of fully-labelled training data, which is unrealistic for most real-world applications. We present a novel approach for completing the supervision of a semi-supervised structure learning task. We incorporate graph-cut minimisation, a technique that derives labels for unlabelled data, based on their distance to their labelled counterparts. In order to adapt graph-cut minimisation to first order logic, we employ a suitable structural distance for measuring the distance between sets of logical atoms. The labelling process is achieved online (single-pass) by means of a caching mechanism and the Hoeffding bound, a statistical tool to approximate globally-optimal decisions from locally-optimal ones. We evaluate our approach on the task of composite event recognition by using a benchmark dataset for human activity recognition, as well as a real dataset for maritime monitoring. The evaluation suggests that our approach can effectively complete the missing labels and eventually, improve the accuracy of the underlying structure learning system.

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Section: Event Calculus and Structure Learningmentioning

confidence: 99%

Semi-supervised online structure learning for composite event recognition

2019

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“…In daily behavior videos, especially in security surveillance videos, a series of noise interferences such as occlusion, illumination changes, and viewing angle changes often occur. At the same time, video content analysis inevitably needs to combine existing experience and knowledge, whereas the existing machine learning algorithms lack the use of background knowledge and the treatment of uncertainty [Katzouris, Michelioudakis, Artikis et al (2018)]. In summary, event recognition often needs to deal with data such as incompleteness, error, inconsistency, and situational changes.…”

Section: Event Recognition Based On Logical Reasoningmentioning

confidence: 99%

Research on Action Recognition and Content Analysis in videos based on DNN and MLN

Song

Zhao

et al. 2019

Computers, Materials &Amp; Continua

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In the current era of multimedia information, it is increasingly urgent to realize intelligent video action recognition and content analysis. In the past few years, video action recognition, as an important direction in computer vision, has attracted many researchers and made much progress. First, this paper reviews the latest video action recognition methods based on Deep Neural Network and Markov Logic Network. Second, we analyze the characteristics of each method and the performance from the experiment results. Then compare the emphases of these methods and discuss the application scenarios. Finally, we consider and prospect the development trend and direction of this field.

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“…Katzouris et al [31] presented online learning of weighted relational rules for complex event recognition. In this study, authors advanced the state-of-the-art by integrating an existing online algorithm for learning crisp relational structure with an online method for weight learning in MLNs.…”

Section: Application Of Srlmentioning

confidence: 99%

Statistical Relational Learning: A State-of-the-Art Review

Kastrati¹,

Biba²

2019

Journal of Engineering Technology and Applied Sciences

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The objective of this paper is to review the state-of-the-art of statistical relational learning (SRL) models developed to deal with machine learning and data mining in relational domains in presence of missing, partially observed, and/or noisy data. It starts by giving a general overview of conventional graphical models, first-order logic and inductive logic programming approaches as needed for background. The historical development of each SRL key model is critically reviewed. The study also focuses on the practical application of SRL techniques to a broad variety of areas and their limitations.

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Online Learning of Weighted Relational Rules for Complex Event Recognition

Cited by 10 publications

References 25 publications

Semi-supervised online structure learning for composite event recognition

Semi-supervised online structure learning for composite event recognition

Research on Action Recognition and Content Analysis in videos based on DNN and MLN

Statistical Relational Learning: A State-of-the-Art Review

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