A Novel Collaborative Optimization Framework for Web Video Event Mining Based on the Combination of Inaccurate Visual Similarity Detection Information and Sparse Textual Information

Zhang, Chengde; Jin, Dandan; Xiao, Xia; Gao, Chen; Shyu, Mei‐Ling

doi:10.1109/access.2020.2964714

Cited by 6 publications

(3 citation statements)

References 36 publications

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“…Zhang et al [25] proposed the methodologies for event mining based on the videos on the web. The number of videos has stored in data repositories and it was extracted by the users in real-time have not been processed effectively due to the size of the file.…”

Section: Literature Surveymentioning

confidence: 99%

Web Information Extraction methods using Web Content Mining (WCM) for Webapplications

Raghavendra¹,

Niranjanamurthy²

2022

IJCDS

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In the digital world era, data was generated by humans and machines are huge in volume and have been accessed through websites on the internet platform. Most of the transactions happened on web product items, web news, and web advertisements. Web Information Extraction (WIE) is the technique where the information on websites is extracted accurately within a time using Web Content Mining (WCM) concept. Every second, new data has been generated in different locations and the contents of the websites have changed rapidly at various intervals during processing time. The live time and location of the data have changed each time when internet users processing web applications. So extracting the information from the web page or website is a challenging one with accuracy and latency on websites. Classic algorithms and data mining techniques are used to preprocess the generated data with a certain time but the validity of those has not been maintained on the web server. Perhaps, their special features have taken for doing extraction using web mining techniques. The recently advanced concepts such as Deep Learning with Recurrent Neural Networks (RNN) are used to perform Web Information Extraction on various websites over the large network by keeping hold of the data status at each second in memory while doing the processing. The technique Long Short-Term Memory (LSTM) is used to hold the status in intermediate memory then all generated data in web applications send this status to RNN for further classifications. Classification methods are used in Artificial Neural Networks (ANN), it would train the input data from the large network and segregate them based on the algorithms used by the user. Finally, the deep learning concept is combined with all recent trends with input models as an embedded layer. Social media information is up-to-date with its originality and validity also keeps track fully in larger networks by using this technique. This paper suggested the best methods to implement the web information extraction concepts in web content mining from different websites on larger clusters/networks using deep learning LSTM techniques.

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Section: Literature Surveymentioning

confidence: 99%

Web Information Extraction methods using Web Content Mining (WCM) for Webapplications

Raghavendra¹,

Niranjanamurthy²

2022

IJCDS

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“…With all these three matrices covering the rows, column information as well as the spread of variance, SVD tries to create a final relatively denser matrix than the initial one to show the association and meaning of terms across documents. SVD is the most widely used technique nowadays in NLP to analyse large terms across different documents [ 25 – 27 ].…”

Section: Event Detection Approachesmentioning

confidence: 99%

A Performance Comparison of Unsupervised Techniques for Event Detection from Oscar Tweets

Malik

Aslam

et al. 2022

Computational Intelligence and Neuroscience

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People’s lives are influenced by social media. It is an essential source for sharing news, awareness, detecting events, people’s interests, etc. Social media covers a wide range of topics and events to be discussed. Extensive work has been published to capture the interesting events and insights from datasets. Many techniques are presented to detect events from social media networks like Twitter. In text mining, most of the work is done on a specific dataset, and there is the need to present some new datasets to analyse the performance and generic nature of Topic Detection and Tracking methods. Therefore, this paper publishes a dataset of real-life event, the Oscars 2018, gathered from Twitter and makes a comparison of soft frequent pattern mining (SFPM), singular value decomposition and k-means (K-SVD), feature-pivot (Feat-p), document-pivot (Doc-p), and latent Dirichlet allocation (LDA). The dataset contains 2,160,738 tweets collected using some seed words. Only English tweets are considered. All of the methods applied in this paper are unsupervised. This area needs to be explored on different datasets. The Oscars 2018 is evaluated using keyword precision (K-Prec), keyword recall (K-Rec), and topic recall (T-Rec) for detecting events of greater interest. The highest K-Prec, K-Rec, and T-Rec were achieved by SFPM, but they started to decrease as the number of clusters increased. The lowest performance was achieved by Feat-p in terms of all three metrics. Experiments on the Oscars 2018 dataset demonstrated that all the methods are generic in nature and produce meaningful clusters.

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“…Jun Wang. Applied Mathematics and Nonlinear Sciences, 9(1) (2024)[1][2][3][4][5][6][7][8][9][10][11][12][13][14] …”

mentioning

confidence: 99%

Exploring the path of sending information about visual contexts of industrial prints

Wang

2023

Applied Mathematics and Nonlinear Sciences

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Aiming at the influence of visual contextual information communication on industrial printmaking, this paper elaborates visual communication theory and establishes a visual information communication model after analyzing the concept, characteristics and components of visual communication, which has a role in visual contextual information communication. The four elements, three important links and the characteristics of visual communication in the process of visual communication are analyzed from the perspective of communication. Based on the above theories, a model of information communication in web interface is constructed. The computational process of the linear binocular cost aggregation algorithm is simplified by using the asymmetric cost aggregation method, and the more robust TAD matching cost function is used to compensate for the lack of accuracy caused by the simplification, and the computational process of the asymmetric linear cost aggregation algorithm is parallelized and mapped to the platform. Experimental data show that the asymmetric cost aggregation approach to linear binocular cost aggregation algorithm can obtain parallax maps in constant running time, and the quality of parallax maps is close to or better than some global matching algorithms which are at the forefront of research, and has a good prospect in the field of real-time binocular vision. The visual web pages designed by this method can better stimulate the user’s desire for acceptance, and the web pages have better visual communication effect and higher practical value, and can create better knowledge benefits in industrial printmaking, with an overall knowledge conversion rate of 85%.

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A Novel Collaborative Optimization Framework for Web Video Event Mining Based on the Combination of Inaccurate Visual Similarity Detection Information and Sparse Textual Information

Cited by 6 publications

References 36 publications

Web Information Extraction methods using Web Content Mining (WCM) for Webapplications

Web Information Extraction methods using Web Content Mining (WCM) for Webapplications

A Performance Comparison of Unsupervised Techniques for Event Detection from Oscar Tweets

Exploring the path of sending information about visual contexts of industrial prints

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