Utilizing affective analysis for efficient movie browsing

Zhang, Shiliang; Tian, Qi; Huang, Qingming; Gao, Wen; Li, Shipeng

doi:10.1109/icip.2009.5413590

Cited by 32 publications

(20 citation statements)

References 12 publications

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“…First, several visual and audio features are extracted from videos to characterize the video content. Then, the extracted features are fed into a general purpose classifier or regressor such as Support Vector Machine (SVM) [15], [23], [12] or Support Vector Regression(SVR) [27], [30], [38], [39], [31] for emotion classification or regression. Table 1 and Table 2 provide an exhaustive summary of direct video affective content analysis works that respectively use continuous emotion dimensions or discrete emotion categories, as well as extracted features, adopted classifiers/regressors, emotion descriptors, size of the dataset (i.e., the number of video clips if not explicitly stated), number of annotators, and experimental results.…”

Section: Discussionmentioning

confidence: 99%

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Video Affective Content Analysis: A Survey of State-of-the-Art Methods

Wang

2015

IEEE Trans. Affective Comput.

163

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Video affective content analysis has been an active research area in recent decades, since emotion is an important component in the classification and retrieval of videos. Video affective content analysis can be divided into two approaches: direct and implicit. Direct approaches infer the affective content of videos directly from related audiovisual features. Implicit approaches, on the other hand, detect affective content from videos based on an automatic analysis of a user's spontaneous response while consuming the videos. This paper first proposes a general framework for video affective content analysis, which includes video content, emotional descriptors, and users' spontaneous nonverbal responses, as well as the relationships between the three. Then, we survey current research in both direct and implicit video affective content analysis, with a focus on direct video affective content analysis. Lastly, we identify several challenges in this field and put forward recommendations for future research.Index Terms-Video affective content analysis, emotion recognition, and content-based video retrieval.

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Section: Discussionmentioning

confidence: 99%

“…Motionrelated features includes motion intensity, motion dynamics, and visual excitement. Motion intensity [30], [38], [14], [22], [12], [48], [39], [31], [42] reflects the smoothness of transitions between frames. It can be estimated from the intensity difference of two frames [31].…”

Section: Visual Featuresmentioning

confidence: 99%

Video Affective Content Analysis: A Survey of State-of-the-Art Methods

Wang

2015

IEEE Trans. Affective Comput.

163

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“…There has been little work on affect analysis in real-world scenarios. One of early work is by Zhang et al [30] who analysed the affect of movies for efficient browsing of videos. However, this method does not take into consideration the facial expressions which are a strong cue.…”

Section: Motivation and Backgroundmentioning

confidence: 99%

Context Based Facial Expression Analysis in the Wild

Dhall

2013

2013 Humaine Association Conference on Affective Computing and Intelligent Interaction

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With the advances in the computer vision in the past few years, analysis of human facial expressions has gained attention. Facial expression analysis is now an active field of research for over two decades now. However, still there are a lot of questions unanswered. This project will explore and devise algorithms and techniques for facial expression analysis in practical environments. Methods will also be developed for inferring the emotion of a group of people. The central hypothesis of the project is that close to real-world data can be extracted from movies and facial expression analysis on movies is a stepping stone for moving to analysis in the real-world. The data extracted from movies carries with it rich meta-data information which will be useful for exploring the role of context in emotion recognition in the wild. For the analysis of groups of people various attributes effect the perception of mood. Study will be conducted and thorough literature survey will be performed on what are these contextual attributes. A system which can classify the mood of a group of people in videos will be developed and will be used to solve the problem of efficient image browsing and retrieval based on emotion.

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“…A limitation of this approach is that the samples contain a single subject only. [18] proposed affect based video clip browsing by learning two regression models, predicting valence and arousal values, to describe the affect. The regression models learnt on an ensemble of audio-video features, such as motion, shot switch rate, frame brightness, pitch, bandwidth, roll off, and spectral flux.…”

Section: Top-down Techniquesmentioning

confidence: 99%

Automatic Group Happiness Intensity Analysis

Dhall

Goecke

Gedeon

2015

IEEE Trans. Affective Comput.

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Abstract-The recent advancement of social media has given users a platform to socially engage and interact with a larger population. Millions of images and videos are being uploaded everyday by users on the web from different events and social gatherings. There is an increasing interest in designing systems capable of understanding human manifestations of emotional attributes and affective displays. As images and videos from social events generally contain multiple subjects, it is an essential step to study these groups of people. In this paper, we study the problem of happiness intensity analysis of a group of people in an image using facial expression analysis. A user perception study is conducted to understand various attributes, which affect a person's perception of the happiness intensity of a group. We identify the challenges in developing an automatic mood analysis system and propose three models based on the attributes in the study. An 'in the wild' image-based database is collected. To validate the methods, both quantitative and qualitative experiments are performed and applied to the problem of shot selection, event summarisation and album creation. The experiments show that the global and local attributes defined in the paper provide useful information for theme expression analysis, with results close to human perception results.

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Utilizing affective analysis for efficient movie browsing

Cited by 32 publications

References 12 publications

Video Affective Content Analysis: A Survey of State-of-the-Art Methods

Video Affective Content Analysis: A Survey of State-of-the-Art Methods

Context Based Facial Expression Analysis in the Wild

Automatic Group Happiness Intensity Analysis

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