Affective content detection using HMMs

Kang, Hang-Bong

doi:10.1145/957052.957066

Cited by 21 publications

(18 citation statements)

References 12 publications

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“…Kang [4] tried to recognize video affective based on HMMs. Videos were classified into three affective genera named happiness, fear and sad by using low-level visual features.…”

Section: Introductionmentioning

confidence: 99%

Horror video recognition based on fuzzy comprehensive evolution

Song

Yang

et al. 2014

China Commun.

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Technique for horror video recognition is important for its application in web content filtering and surveillance, especially for preventing children from being threaten. In this paper, a novel horror video recognition algorithm based on fuzzy comprehensive evolution model is proposed. Three low-level video features are extracted as typical features, and they are video key-light, video colour energy and video rhythm. Analytic Hierarchy Process (AHP) is adopted to estimate the weights of extracted features in fuzzy evolution model. Horror evaluation (membership function) is on shot scale and it is constructed based on the knowledge that videos which share the same affective have similar low-level features. K-Means algorithm is implemented to help finding the most representative feature vectors. The experimental results demonstrate that the proposed approach has good performance in recognition precision, recall rate and F1 measure.

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“…Kang [4] tried to recognize video affective based on HMMs. Videos were classified into three affective genera named happiness, fear and sad by using low-level visual features.…”

Section: Introductionmentioning

confidence: 99%

Horror video recognition based on fuzzy comprehensive evolution

Song

Yang

et al. 2014

China Commun.

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“…Most existing methods focus on detecting movie affective content by using low-level features [2,3,4,5]. Kang used HMMs to categorize movie scenes into three types of affective content, joy, fear, and sadness, based on low-level visual features [2]. Xu et al proposed an HMM-based method to detect affective events such as laughter in comedies and terrible sounds in horror movies [3].…”

Section: Introductionmentioning

confidence: 99%

Latent topic driving model for movie affective scene classification

Irie

Hidaka²,

Satou

et al. 2009

Proceedings of the 17th ACM International Conference on Multimedia

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This paper proposes a latent topic driving model (LTDM) as a novel approach to movie affective scene classification. LTDM is a discriminative model of emotions driven by movie affective contents. Unlike existing methods, our approach is based on movie topic extraction via the latent Dirichlet allocation (LDA) and emotion dynamics modeling with reference to Plutchik's emotion theory. The classification procedure starts by segmenting movie scenes into movie shots, each of which is represented by a histogram of quantized affect-related audio-visual features. LDA is applied to detect topics of each movie shot. Emotions for the current movie shot are estimated based on both the topics of the shot and emotion transition weights determined by Plutchik's emotion theory. We conduct experiments using 206 movie scenes extracted from 24 movie titles (total 6 hours 20 min. 12 sec.) and the labels of eight emotion categories given by 16 subjects are collected. The results show that LTDM outperforms conventional modeling approaches in terms of the subject agreement rate.

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“…These works try to provide an effective and efficient way to manage and access multimedia databases. More recently, researchers have revealed the significance of affective analysis from a personalized media point of view [1][2][3][4]. For example, many users favor a flexible tool to quickly browse the funniest or the most sentimental segments of a movie, as well as the most exciting parts of a sports game video.…”

Section: Introductionmentioning

confidence: 99%

“…fear, sadness and joy). The users' feedbacks [3] were further used to adjust weights to emphasize different features in [2]. In [4], four sound energy events were identified that convey well established meanings through their dynamics.…”

Section: Introductionmentioning

confidence: 99%

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Affective Content Analysis in Comedy and Horror Videos by Audio Emotional Event Detection

Chia

Jin

2005 IEEE International Conference on Multimedia and Expo

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We study the problem of affective content analysis. In this paper we think of affective contents as those video/audio segments, which may cause an audience's strong reactions or special emotional experiences, such as laughing or fear. Those emotional factors are related to the users' attention, evaluation, and memories of the content. The modeling of affective effects depends on the video genres. In this work, we focus on comedy and horror films to extract the affective content by detecting a set of so-called audio emotional events (AEE) such as laughing, horror sounds, etc. Those AEE can be modeled by various audio processing techniques, and they can directly reflect an audience's emotion. We use the AEE as a clue to locate corresponding video segments. Domain knowledge is more or less employed at this stage. Our experimental dataset consists of 40-minutes comedy video and 40-minutes horror film. An average recall and precision of above 90% is achieved. It is shown that, in addition to rich visual information, an appropriate usage of special audios is an effective way to assist affective content analysis.

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Affective content detection using HMMs

Cited by 21 publications

References 12 publications

Horror video recognition based on fuzzy comprehensive evolution

Horror video recognition based on fuzzy comprehensive evolution

Latent topic driving model for movie affective scene classification

Affective Content Analysis in Comedy and Horror Videos by Audio Emotional Event Detection

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