A study on video viewing behavior: application to movie trailer miner

Mongy, Sylvain; Djeraba, Chabane

doi:10.1117/12.707593

Cited by 5 publications

(5 citation statements)

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“…It should be noted that similar log data have been used in the past for such diverse tasks as effective storage of video data in computer networks (Acharya, Smith, & Parnes, 2000), analyzing the reaction that videos have on viewers (Mongy & Djeraba, 2007) and the design of novel video playback interfaces (Crockford & Agius, 2006). To the best of our knowledge, however, we are the first to examine how individual attention capabilities reflect in these data.…”

Section: Methodsmentioning

confidence: 99%

Viewing the Viewers

et al. 2012

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Section: Methodsmentioning

confidence: 99%

Viewing the Viewers

et al. 2012

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“…Hidden Markov Models (HMM) have been used to infer user behavioral states while using a video browser (Mongy, 2007;Westphal and Syeda-mahmood, 2002). Sequences of user interactions (e.g., ''Play'', ''Stop'') are used to infer browsing states (e.g., ''Reading'').…”

Section: State Of the Artmentioning

confidence: 99%

Activity recognition using eye-gaze movements and traditional interactions

Courtemanche

Aïmeur

Dufresne

et al. 2011

Interacting with Computers

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a b s t r a c tThe need for intelligent HCI has been reinforced by the increasing numbers of human-centered applications in our daily life. However, in order to respond adequately, intelligent applications must first interpret users' actions. Identifying the context in which users' interactions occur is an important step toward automatic interpretation of behavior. In order to address a part of this context-sensing problem, we propose a generic and application-independent framework for activity recognition of users interacting with a computer interface. Our approach uses Layered Hidden Markov Models (LHMM) and is based on eye-gaze movements along with keyboard and mouse interactions. The main contribution of the proposed framework is the ability to relate users' interactions to a task model in variant applications and for different monitoring purposes. Experimental results from two user studies show that our activity recognition technique is able to achieve good predictive accuracy with a relatively small amount of training data.

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“…Some works focus on the transition probability of different user behaviors. In [19], the K-means technique is used to retrieve and cluster user behaviors using a Markovian model based on a movie trailer database. Another work [20] probes the relationship between several types of user behavior and uncovers that the behavior of one individual user in a video streaming session has strong correlation with the user's behaviors in previous streaming sessions.…”

Section: Related Workmentioning

confidence: 99%

Smart Streaming for Online Video Services

Chen

Zhou

Chiu

2015

IEEE Trans. Multimedia

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Bandwidth consumption is a significant concern for online video service providers. Practical video streaming systems usually use some form of HTTP streaming (progressive download) to let users download the video at a faster rate than the video bitrate. Since users may quit before viewing the complete video, however, much of the downloaded video will be "wasted". To the extent that users' departure behavior can be predicted, we develop smart streaming that can be used to improve user QoE with limited server bandwidth or save bandwidth cost with unlimited server bandwidth. Through measurement, we extract certain user behavior properties for implementing such smart streaming, and demonstrate its advantage using prototype implementation as well as simulations.

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A study on video viewing behavior: application to movie trailer miner

Cited by 5 publications

References 0 publications

Viewing the Viewers

Viewing the Viewers

Activity recognition using eye-gaze movements and traditional interactions

Smart Streaming for Online Video Services

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