Affective video content representation and modeling

By Alan Hanjalic, Li-Qun Xu. IEEE transactions on multimedia, 2005

629 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Experimental and Cognitive Psychology

View Paper: https://www.semanticscholar.org/paper/6c015d547c56db99388faa29a0628b86f318e1d3

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

In Experimental and Cognitive Psychology

Citations in the corpus

Concept-Based Video Retrieval (2009, background): “This is especially true when the user is making subjective interpretations of the video data related to feelings or emotions, for example, by describing a scene as romantic or hilarious [76].”
DECAF: MEG-Based Multimodal Database for Decoding Affective Physiological Responses (2015, positive): “DECAF also contains time-continuous emotion annotations for movie clips from seven users, which we use to demonstrate dynamic emotion prediction.”
Automatic mood detection and tracking of music audio signals (2006, background): “Therefore, the control dimension is neglected in this paper, as it was in [35].”
Exploring Principles-of-Art Features For Image Emotion Recognition (2014, methodology): “Similar to [24], we only modelled on the valence and arousal dimension, without considering the dominance dimension for its relatively small contributing scope on emotions [11].”
Multimodal Emotion Recognition in Response to Videos (2012, background): “[3] M. Soleymani, G. Chanel, J. J. M. Kierkels, and T. Pun,“ Affective Characterization of Movie Scenes Based on Content Analysis and Physiological Changes,”International Journal of Semantic Computing, vol. 3, no. 2, pp. 235-254, June 2009.”
Video summarisation: A conceptual framework and survey of the state of the art (2008, methodology): “A number of techniques [40,55–57] are specific to sports video content.”
Multimodal Human Computer Interaction: A Survey (2005, background): “The different emotional labels could be plotted at various positions on a two-dimensional plane spanned by these two axes to construct a 2D emotion model [35][23].”
DEAP: A Database for Emotion Analysis ;Using Physiological Signals (2012, background): “Hanjalic and Xu [29] introduced " personalized content delivery " as a valuable tool in affective indexing and retrieval systems.”
“Naturally” Less Exciting? Visual Production of Men's and Women's Track and Field Coverage During the 2004 Olympics (2009, background): “Research supports this assumption ( Hanjalic & Xu, 2005 ).”
A Survey on Visual Content-Based Video Indexing and Retrieval (2011, positive): “To combine affective semantics from the spoken language in the audio track with visual affective semantics [75], to utilize cognitive models, cultural backgrounds, aesthetic criteria, cold and warm tones,”
Affective e-Learning: Using "Emotional" Data to Improve Learning in Pervasive Learning Environment (2009, methodology): “For instance, video content with emotion tags were modeled to support the automatic generation of 'video highlights' or personalized recommendations for video films (Hanjalic & Xu, 2005).”
Affective image classification using features inspired by psychology and art theory (2010, background): “[ 12 ]) to 35 \impression words" (in [14]).”