Novel Shot Boundary Detection in News Streams Based on Fuzzy Petri Nets

Yang, Shu-Hung; Lin, Yi-Nan; Chiou, Gwo‐Jen; Chen, Ming-Kuen; Shen, Victor R.L.; Tseng, Hsin-Yi

doi:10.1080/08839514.2019.1661118

Cited by 11 publications

(2 citation statements)

References 11 publications

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“…The average edge information of the gradual curves in the sequence of frames is obtained from the optimal edge detector in order to detect the gradual transition. Yuang et al 18 proposed a high‐level fuzzy Petri net model for the detection of shot boundaries. Due to the variations in frame's brightness within another scene, the frame of the video in the flash was changed.…”

Section: Related Workmentioning

confidence: 99%

Hybrid feature extraction and optimized deep convolutional neural network based video shot boundary detection

Mishra

2022

Concurrency and Computation

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Summary This paper presents a hybrid feature extraction and optimized deep learning model for the effective shot boundary detection (SBD). A cross‐guided bilateral filtering technique is used for pre‐processing. A hybrid fuzzy histogram with dual tree complex wavelet transform (FH‐DTCWT) is developed for the feature extraction to extract the visual features from the pre‐processed frames of the block. Candidate segment selection process is performed to identify the non‐boundary frames from the extracted features to improve the accuracy. Continuity matrix is created using the possible transition frames to verify that the frames are in sequential order without any gap. Classification of transition types are performed by the proposed optimized deep convolutional neural network. Optimized deep learning (DCNN‐RBESO) model is the combination of deep convolutional neural network (DCNN) and Rider bald eagle search optimization (RBESO) algorithm. To update the weight of DCNN model in learning, RBESO is utilized. MATLAB tool is used to develop the proposed SBD model. The experimental analysis is carried out for the proposed model using TREC Video Retrieval Evaluation (TRECVID) and VideoSeg datasets. The proposed model outperformed in the detection of shot boundaries in terms of precision, recall, and F1‐score.

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Section: Related Workmentioning

confidence: 99%

Hybrid feature extraction and optimized deep convolutional neural network based video shot boundary detection

Mishra

2022

Concurrency and Computation

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“…Moreover, FPNs can capture the dynamic nature and improve the efficiency of fuzzy rule‐based reasoning by marking evolution (Ha et al, 2007; Liu, Liu, et al, 2013; Suraj, 2013). In recent years, owing their strong ability to depict uncertain knowledge and support reasoning processes, the FPNs have been widely used in various fields for chemical plant risk assessment (Zhou & Reniers, 2020), aluminium electrolysis cell condition identification (Yue et al, 2020), shot boundary detection in news streams (Yang et al, 2019), energy efficient operation of a manufacturing system (Wang et al, 2019), failure mode and effect analysis (Li, Xiong, et al, 2019; Shi et al, 2020), process equipment failure risk assessment (Li, He, et al, 2019), and so on.…”

Section: Introductionmentioning

confidence: 99%

Knowledge representation and acquisition using R‐numbers Petri nets considering conflict opinions

et al. 2021

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As a vital modelling technique, fuzzy Petri nets (FPNs) have been widely used in various areas for knowledge representation and reasoning. However, the conventional FPNs have many deficiencies in representing inaccurate knowledge, acquiring knowledge parameters and conducting approximate reasoning when used in the real world. In this article, a new version of FPNs, called R‐numbers Petri nets (RPNs), is proposed to overcome the shortcomings and enhance the effectiveness of FPNs. Based on R‐numbers, expert knowledge is depicted in the form of weighted R‐numbers production rules. The interrelationships among input places (or transitions) are modelled by the R‐numbers Maclaurin symmetric mean operator in the knowledge reasoning process. In addition, the conflict opinions of experts are handled with the proposed RPN model in order to obtain more precise knowledge parameters. Finally, the effectiveness and practicality of the proposed RPNs are illustrated by a realistic example concerning reliability analysis of an electric vehicle motor.

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FPNs for Knowledge Representation and Reasoning: A Literature Review

Shi,

Liu

2023

Fuzzy Petri Nets for Knowledge Representation, Acquisition and Reasoning

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Novel Shot Boundary Detection in News Streams Based on Fuzzy Petri Nets

Cited by 11 publications

References 11 publications

Hybrid feature extraction and optimized deep convolutional neural network based video shot boundary detection

Hybrid feature extraction and optimized deep convolutional neural network based video shot boundary detection

Knowledge representation and acquisition using R‐numbers Petri nets considering conflict opinions

FPNs for Knowledge Representation and Reasoning: A Literature Review

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