Video Semantic Event/Concept Detection Using a Subspace-Based Multimedia Data Mining Framework

Shyu, Mei‐Ling; Xie, Zongxing; Chen, Min; Chen, Shu‐Ching

doi:10.1109/tmm.2007.911830

Cited by 120 publications

(46 citation statements)

References 18 publications

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“…and then by using a classifier, the events or high level semantics (high level features) are detected. In [7], an automatic method is proposed that utilizes a subspace-based data mining method for feature extraction. That method is generic such that it does not use any prior knowledge in the detection process and can be considered as a domain-free method.…”

Section: Literature Review (Soccer Game As Case Study)mentioning

confidence: 99%

Video Summarization: Survey on Event Detection and Summarization in Soccer Videos

Khan¹,

Pawar²

2015

ijacsa

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Abstract-In today's world, the rapid development of digital video and editing technology has led to fast growing of video data, creating the need for effective and advanced techniques for analysis and video retrieval, as multimedia repositories have made browsing, delivery of contents (video) and video retrieval very slow. Hence, video summarization proposes various ways for faster browsing among a large amount of data and also for content indexing. Many people spend their free time to watch or play different sports like soccer, cricket, etc. but it is not possible to watch each and every game due to the longer timing of the game. In such cases, the users may just want to view the summary of the video that is just an abstract of the original video, instead of watching the whole video that provides more information about the occurrence of various incidents in the video. It is preferable to watch just highlights of the game or just review/trailer of a movie. Apparently, summarizing a video is an important process. In this paper, video summarization approaches are discussed, that can generate static or dynamic summaries. We present different techniques for each mode in literature. We have discussed some features used for generating video summaries. As soccer is the world's most famous game played and watched, it is taken as a case study. Research done in this domain is discussed. We conclude that there is a broad perspective for further research in this field.

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Section: Literature Review (Soccer Game As Case Study)mentioning

confidence: 99%

Video Summarization: Survey on Event Detection and Summarization in Soccer Videos

Khan¹,

Pawar²

2015

ijacsa

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“…Machine Learning has been effective in most of the domain-specific applications. However, in domain-independent situations, training and class-imbalance become real issues [33].…”

Section: Related Workmentioning

confidence: 99%

A framework for automatic semantic video annotation

Altadmri

Ahmed

2013

Multimed Tools Appl

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The rapidly increasing quantity of publicly available videos has driven research into developing automatic tools for indexing, rating, searching and retrieval. Textual semantic representations, such as tagging, labelling and annotation, are often important factors in the process of indexing any video, because of their user-friendly way of representing the semantics appropriate for search and retrieval. Ideally, this annotation should be inspired by the human cognitive way of perceiving and of describing videos. The difference between the low-level visual contents and the corresponding human perception is referred to as the 'semantic gap'. Tackling this gap is even harder in the case of unconstrained videos, mainly due to the lack of any previous information about the analyzed video on the one hand, and the huge amount of generic knowledge required on the other. This paper introduces a framework for the Automatic Semantic Annotation of unconstrained videos. The proposed framework utilizes two non-domain-specific layers: low-level visual similarity matching, and an annotation analysis that employs commonsense knowledgebases. Commonsense ontology is created by incorporating multiple-structured semantic relationships. Experiments and black-box tests are carried out on standard video databases for action recognition and video information retrieval. White-box tests examine the performance of the individual intermediate layers of the framework, and the evaluation of the results and the statistical analysis show that integrating visual similarity matching with commonsense semantic relationships provides an effective approach to automated video annotation.

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“…Multimedia Temporal Analysis and Ensemble Learning: MCA (Multiple Correspondence Analysis) has been successfully applied to various multimedia analysis tasks, such as feature selection [11], discretization [12], data pruning [13], classification [14] and video semantic concept detection [15]. Inspired by our HCFG algorithm and the information gain analysis method, an IF-MCA modeling approach is proposed with MapReduce implementation to deal with large-scale multimedia data.…”

Section: Proposed Solutionsmentioning

confidence: 99%

“…To address this issue, research efforts have been directed towards various essential aspects like feature selection [48,49,50,21], training data selection [51,15], and classifier selection/fusion [52,53]. Among them, feature selection is considered especially applicable in big data analysis because it eliminates features with little predictive information, which also reduces the dimensionality of data and allows the learning algorithms to operate faster and more effectively [50].…”

Section: Feature Selectionmentioning

confidence: 99%

“…In particular, Multiple Correspondence Analysis (MCA) has shown to be able to capture the correlation between the features and the classes [13], and has been successfully applied to various multimedia analysis, including classification [14] and video semantic concept detection [15]. In brief, MCA extends the standard Cor- To further reduce the computational time for big data, parallel computing is often adopted to simultaneously utilize distributed resources for a computation task.…”

Section: Feature Selectionmentioning

confidence: 99%

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Exploring Hidden Coherent Feature Groups and Temporal Semantics for Multimedia Big Data Analysis

Yang¹

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Video Semantic Event/Concept Detection Using a Subspace-Based Multimedia Data Mining Framework

Cited by 120 publications

References 18 publications

Video Summarization: Survey on Event Detection and Summarization in Soccer Videos

Video Summarization: Survey on Event Detection and Summarization in Soccer Videos

A framework for automatic semantic video annotation

Exploring Hidden Coherent Feature Groups and Temporal Semantics for Multimedia Big Data Analysis

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