Analyzing user's behavior on a video database

Mongy, Sylvain; Bouali, Fatma; Djeraba, Chabane

doi:10.1145/1133890.1133902

Cited by 7 publications

(6 citation statements)

References 12 publications

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“…The video knowledge is explored in a two stages, the first being the video content processing in which the video clip is segmented into certain analysis units extracting their representative features and the second being the video association mining that extracts the knowledge from the feature descriptors. Mongy et al [66] presented a framework for video usage mining to generate user profiles on a video search engine in the context of movie production that analyzes the user behaviors on a set of video data to create suitable tools to help people in browsing and searching a large amount of video data.…”

Section: Video Association Miningmentioning

confidence: 99%

A study on video data mining

Vijayakumar

Nedunchezhian²

2012

Int J Multimed Info Retr

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Data mining is a process of extracting previously unknown knowledge and detecting the interesting patterns from a massive set of data. Thanks to the extensive use of information technology and the recent developments in multimedia systems, the amount of multimedia data available to users has increased exponentially. Video is an example of multimedia data as it contains several kinds of data such as text, image, meta-data, visual and audio. It is widely used in many major potential applications like security and surveillance, entertainment, medicine, education programs and sports. The objective of video data mining is to discover and describe interesting patterns from the huge amount of video data as it is one of the core problem areas of the data-mining research community. Compared to the mining of other types of data, video data mining is still in its infancy. There are many challenging research problems existing with video mining. Beginning with an overview of the video datamining literature, this paper concludes with the applications of video mining.

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Section: Video Association Miningmentioning

confidence: 99%

A study on video data mining

Vijayakumar

Nedunchezhian²

2012

Int J Multimed Info Retr

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“…Personality traits, facets and states -Can be classified as: domain-specific [3], [9], [17], [18] or generic [1], [2], [4], [7]- [9], [11] - [13]. Both domain-specific and generic traits can be cognitive [3], [4], [8], [12], [17], [18], or affective [2], [3], [7], [9], [11]- [13], [17], [18].…”

Section: Personality Modelsmentioning

confidence: 99%

“…Both domain-specific and generic traits can be cognitive [3], [4], [8], [12], [17], [18], or affective [2], [3], [7], [9], [11]- [13], [17], [18]. Existing CARPE research did not really address this level, but for other application areas, formal, generic personality models are emerging in [11] - [13].…”

Section: Personality Modelsmentioning

confidence: 99%

“…Existing CARPE research did not really address this level, but for other application areas, formal, generic personality models are emerging in [11] - [13]. Domain specific personality behavior and classifying models are emerging in media semantics and data mining [18].…”

Section: Personality Modelsmentioning

confidence: 99%

“…Popularly researched area, mainly for uncovering domain specific traits, e.g. how the person is using the video recorder/player [18] and what are the sentiments in relation to commercial products [17]. With increasing success in visual object and speech recognition, their substitution with text strings can broaden the scope of text mining.…”

Section: Personality Profiling Integrationmentioning

confidence: 99%

See 2 more Smart Citations

Why and how CARPE should be personal?

Seymour¹

2005

Proceedings of the 2nd ACM Workshop on Continuous Archival and Retrieval of Personal Experiences

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In this paper, the significance of modeling human personalities is analyzed for improving the value proposition of solutions for Capturing, Archiving and Retrieval of Personal Experiences (CARPE). Differences in human personalities have a significant and often an overriding impact on how raw events are linked and organized into threads of experiences. Recommendations and predictions are presented on how existing technologies and current research can be integrated for this purpose. The focus here is on the importance of using techniques for improving the quality of the experience thread formation from the raw stream of observation content and on harvesting personality information from analyzing the captured content. Personality models are used when clustering raw event content into such threads. The resulting threads will represent the subject's experience in view of the subjects' personality. The formations of the CARPE threads also contribute to the development of the personality. As a result, the real-life experience view of the subsequently built threads is further enhanced. Also discussed are ways of integrating various methods of building and integrating such models using personality profiling.

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Using Automatic Facial Expression Classification for Contents Indexing Based on the Emotional Component

Kowalik

Aoki

Yasuda

2006

Embedded and Ubiquitous Computing

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Within the last decade the development of new technologies in the multimedia sector has advanced with stunning pace. Due to the availability of high-capacity mass storage devices at low cost private multimedia libraries containing digital video and audio items have recently gained popularity. Although attached meta-data like title, actor's/actress' name and creation time eases the task of finding preferred contents, it is still difficult to find a specific part within a movie one enjoyed before by remembering the time code. In this paper we introduce the BROAFERENCE system that provides a solution for the above problem. We propose meta-data creation based on recorded user experience derived from facial expressions containing joy, sadness and anger events as well as interest focus data. In the following the system layout, functionality and conducted experiments for system verification will be introduced to the reader.

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Analyzing user's behavior on a video database

Cited by 7 publications

References 12 publications

A study on video data mining

A study on video data mining

Why and how CARPE should be personal?

Using Automatic Facial Expression Classification for Contents Indexing Based on the Emotional Component

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