Semi-supervised evolutionary ensembles for Web video categorization

Mahmood, Amjad; Li, Tianrui; Yang, Yan; Wang, Hongjun; Afzal, Mehtab

doi:10.1016/j.knosys.2014.11.030

Cited by 19 publications

(6 citation statements)

References 38 publications

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“…In their work, video co‐watch data were exploited, whereby through local graph clustering, weakly labeled videos were chosen for classifier training. In semi‐supervised evolutionary ensembles for Web video categorization, clustering ensemble process was iterated with the assistance of Genetic algorithm guided by semantic similarity and pre‐paired percentage …”

Section: Related Workmentioning

confidence: 99%

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Web video classification with visual and contextual semantics

Afzal

Shah

Tufail

2019

Int J Communication

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Summary On the social Web, the amount of video content either originated from wireless devices or previously received from media servers has increased enormously in the recent years. The astounding growth of Web videos has stimulated researchers to propose new strategies to organize them into their respective categories. Because of complex ontology and large variation in content and quality of Web videos, it is difficult to get sufficient, precisely labeled training data, which causes hindrance in automatic video classification. In this paper, we propose a novel content‐ and context‐based Web video classification framework by rendering external support through category discriminative terms (CDTs) and semantic relatedness measure (SRM). Mainly, a three‐step framework is proposed. Firstly, content‐based video classification is proposed, where twofold use of high‐level concept detectors is leveraged to classify Web videos. Initially, category classifiers induced from VIREO‐374 detectors are trained to classify Web videos, and then concept detectors with high confidence for each video are mapped to CDT through SRM‐assisted semantic content fusion function to further boost the category classifiers, which intuitively provide a more robust measure for Web video classification. Secondly, a context‐based video classification is proposed, where twofold use of contextual information is also harnessed. Initially, cosine similarity and then semantic similarity are measured between text features of each video and CDT through vector space model (VSM)‐ and SRM‐assisted semantic context fusion function, respectively. Finally, classification results from content and context are fused to compensate for the shortcomings of each other, which enhance the video classification performance. Experiments on large‐scale video dataset validate the effectiveness of the proposed solution.

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Section: Related Workmentioning

confidence: 99%

“…In semi-supervised evolutionary ensembles for Web video categorization, clustering ensemble process was iterated with the assistance of Genetic algorithm guided by semantic similarity and pre-paired percentage. 27…”

Section: Web Video Classification Using Text Featuresmentioning

confidence: 99%

Web video classification with visual and contextual semantics

Afzal

Shah

Tufail

2019

Int J Communication

Self Cite

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“…This section represents some related previous works which are implemented to classify web videos using metadata. The authors Amjad Mahmood, Tianrui Li, Yan Yang, Hongjun Wang and Mehtab Afzal [1], worked on categorization of web videos based on textual metadata. The proposed techniques to categorize web videos are based on Semi-supervised Evolutionary Ensemble (SS-EE) framework.…”

Section: Related Workmentioning

confidence: 99%

“…As a part of knowledge discovery from web videos, the classification of web videos is an increasingly outstanding area of research, growing with the quantity of videos shared through social sites such as YouTube, Yahoo Screen etc. As much as its importance, web based video classification poses serious challenges to computer vision researchers [1].…”

Section: Introductionmentioning

confidence: 99%

Web Video Mining: Metadata Predictive Analysis using Classification Techniques

Algur¹,

Bhat²

2016

IJITCS

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Now a days, the Data Engineering becoming emerging trend to discover knowledge from web audiovisual data such as-YouTube videos, Yahoo Screen, Face Book videos etc. Different categories of web video are being shared on such social websites and are being used by the billions of users all over the world. The uploaded web videos will have different kind of metadata as attribute information of the video data. The metadata attributes defines the contents and features/characteristics of the web videos conceptually. Hence, accomplishing web video mining by extracting features of web videos in terms of metadata is a challenging task. In this work, effective attempts are made to classify and predict the metadata features of web videos such as length of the web videos, number of comments of the web videos, ratings information and view counts of the web videos using data mining algorithms such as Decision tree J48 and navie Bayesian algorithms as a part of web video mining. The results of Decision tree J48 and navie Bayesian classification models are analyzed and compared as a step in the process of knowledge discovery from web videos.

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“…The amount of video content on the Internet has amplified dramatically in the recent years. New generation of high speed Internet connectivity, ubiquitous use of smart phone devices, and the popularity of video websites such as YouTube, Yahoo Screen etc, are being contribute to the rapid increase of video content over the Internet [1]. According to the surprising statistic of the YouTube [2], the web videos can be considered as ‗Big Data'.…”

Section: Introductionmentioning

confidence: 99%

Web Video Object Mining: Expectation Maximization and Density Based Clustering of Web Video Metadata Objects

Algur¹,

Bhat²

2016

IJIEEB

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Nowadays YouTube becoming most popular video sharing website, and is established in 2005. The YouTube official website is providing different categories videos including Science and Technology, Films and Animation, News and politics, Movies, Comedy, Sports, Music etc. Each video hosted in website such as YouTube have its own identity and features. The identity and features of each video can be described by web video metadata objects such as-URL of each video, category, length of the video, rating information, view counts, comment information, key words etc. Using extracted web video metadata objects, we present an indepth and systematic clustering study on the metadata objects of YouTube videos using Expectation Maximization (EM) and Density Based (DB) clustering approach. Distinct web video metadata object clusters are formed based on different category of web videos. The resultant clusters are analyzed in depth as a step in the KDD process.

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Semi-supervised evolutionary ensembles for Web video categorization

Cited by 19 publications

References 38 publications

Web video classification with visual and contextual semantics

Web video classification with visual and contextual semantics

Web Video Mining: Metadata Predictive Analysis using Classification Techniques

Web Video Object Mining: Expectation Maximization and Density Based Clustering of Web Video Metadata Objects

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