Affective Recommendation of Movies Based on Selected Connotative Features

Canini, Luca; Benini, Sergio; Leonardi, Riccardo

doi:10.1109/tcsvt.2012.2211935

Cited by 85 publications

(65 citation statements)

References 51 publications

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“…We first show how to extract low-level and grammar features related to style and link them to emotional responses gathered from the audience, and how to exploit these characteristics for recommending videos [7,8]. We then focus on one of these stylistic features, the scale of shot, which Cognitive Film Theory links to the film's emotional effect on the viewer [9] and propose automatic pipelines for its computation.…”

Section: Tackling Movie Semanticsmentioning

confidence: 99%

“…To reduce the gap between the objective level of video features and the subjective sphere of emotions, in [7,8] we propose to shift the representation towards the connotative properties of movies.…”

Section: Motivation and Aimsmentioning

confidence: 99%

“…While in [7] connotative rates are assigned by users, in the study in [8] we aim at predicting connotative values directly using audiovisual features. Figure 8 presents the modelling approach to establish a relation between connotative rates assigned by users and video characteristics.…”

Section: Recommendation Based On Connotative Featuresmentioning

confidence: 99%

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Movies and meaning: from low-level features to mind reading

Benini¹

2017

Self Cite

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When dealing with movies, closing the tremendous discontinuity between low-level features and the richness of semantics in the viewers' cognitive processes, requires a variety of approaches and different perspectives. For instance when attempting to relate movie content to users' affective responses, previous work suggests that a direct mapping of audio-visual properties into elicited emotions is difficult, due to the high variability of individual reactions. To reduce the gap between the objective level of features and the subjective sphere of emotions, we exploit the intermediate representation of the connotative properties of movies: the set of shooting and editing conventions that help in transmitting meaning to the audience. One of these stylistic feature, the shot scale, i.e. the distance of the camera from the subject, effectively regulates theory of mind, indicating that increasing spatial proximity to the character triggers higher occurrence of mental state references in viewers' story descriptions. Movies are also becoming an important stimuli employed in neural decoding, an ambitious line of research within contemporary neuroscience aiming at "mindreading". In this field we address the challenge of producing decoding models for the reconstruction of perceptual contents by combining fMRI data and deep features in a hybrid model able to predict specific video object classes.

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Section: Tackling Movie Semanticsmentioning

confidence: 99%

Section: Motivation and Aimsmentioning

confidence: 99%

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Movies and meaning: from low-level features to mind reading

Benini¹

2017

Self Cite

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“…In [2], Irie et al propose to represent movie shots with so-called Bag-of-Affective Audiovisual Words and apply a latent topic driving model in order to map these representations to affective categories. In [17], Canini et al introduce a framework where movie scenes are represented in a 3-dimensional connotative space whose dimensions are natural, temporal, and energetic. The aim is to reduce the gap between objective low-level audio-visual features and highly subjective emotions through connotation.…”

Section: Related Workmentioning

confidence: 99%

Fusion of learned multi-modal representations and dense trajectories for emotional analysis in videos

Acar

Hopfgartner

Albayrak

2015

2015 13th International Workshop on Content-Based Multimedia Indexing (CBMI)

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Abstract-When designing a video affective content analysis algorithm, one of the most important steps is the selection of discriminative features for the effective representation of video segments. The majority of existing affective content analysis methods either use low-level audio-visual features or generate handcrafted higher level representations based on these lowlevel features. We propose in this work to use deep learning methods, in particular convolutional neural networks (CNNs), in order to automatically learn and extract mid-level representations from raw data. To this end, we exploit the audio and visual modality of videos by employing Mel-Frequency Cepstral Coefficients (MFCC) and color values in the HSV color space. We also incorporate dense trajectory based motion features in order to further enhance the performance of the analysis. By means of multi-class support vector machines (SVMs) and fusion mechanisms, music video clips are classified into one of four affective categories representing the four quadrants of the Valence-Arousal (VA) space. Results obtained on a subset of the DEAP dataset show (1) that higher level representations performs better than low-level features, and (2) that incorporating motion information leads to a notable performance gain, independently from the chosen representation.

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“…Some focus on distilling the influence of specific cinematographic theories [11], types of segment and shot [12], the use of colour [13] and connotative space [14]. Others focus on modelling these different audio-visual features to predict emotions [15].…”

Section: Related Workmentioning

confidence: 99%

Modelling the influence of personality and culture on affect and enjoyment in multimedia

Guntuku

Wang

Scott

et al. 2015

2015 International Conference on Affective Computing and Intelligent Interaction (ACII)

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Abstract-Affect is evoked through an intricate relationship between the characteristics of stimuli, individuals, and systems of perception. While affect is widely researched, few studies consider the combination of multimedia system characteristics and human factors together. As such, this paper explores the influence of personality (Five-Factor Model) and cultural traits (Hofstede Model) on the intensity of multimedia-evoked positive and negative affects (emotions). A set of 144 video sequences (from 12 short movie clips) were evaluated by 114 participants from a cross-cultural population, producing 1232 ratings. On this data, three multilevel regression models are compared: a baseline model that only considers system factors; an extended model that includes personality and culture; and an optimistic model in which each participant is modelled. An analysis shows that personal and cultural traits represent 5.6% of the variance in positive affect and 13.6% of the variance in negative affect. In addition, the affect-enjoyment correlation varied across the clips. This suggests that personality and culture play a key role in predicting the intensity of negative affect and whether or not it is enjoyed, but a more sophisticated set of predictors is needed to model positive affect with the same efficacy.

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Affective Recommendation of Movies Based on Selected Connotative Features

Cited by 85 publications

References 51 publications

Movies and meaning: from low-level features to mind reading

Movies and meaning: from low-level features to mind reading

Fusion of learned multi-modal representations and dense trajectories for emotional analysis in videos

Modelling the influence of personality and culture on affect and enjoyment in multimedia

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