Competence Center Information Retrieval and Machine Learning

Detection of Violent Scenes using Affective Features

Esra Acar

4. October 2012

Detection of Violent Scenes using Affective Features

Outline

▶ Motivation▶ Background▶ The Method

Audio Features Visual Features

▶ Results & Discussion▶ Conclusions & Future Work

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Detection of Violent Scenes using Affective Features

Motivation

▶ The MediaEval 2012 Affect Task aims at detecting violent segments in movies.

▶ A recent work on horror scene recognition detects horror scenes by affect-related features.

▶ We investigate whether affect-related features provide good representation of

violence, and making abstractions from low-level features is better than

directly using low-level data.

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Detection of Violent Scenes using Affective Features

Background

▶ The affective content of a video corresponds to the intensity (i.e. arousal), and the type (i.e. valence) of emotion expected to arise in the user while watching that video.

▶ Recent research efforts propose methods to map low-level features to high-level emotions.

▶ Film-makers intend to elicit some particular emotions (i.e. expected emotions) in the audience.

▶ When we refer to violence as an expected emotion in videos, affect-related features are applicable for violence detection.

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Detection of Violent Scenes using Affective Features

The Method

▶ The method uses affect-related audio and visual features to represent violence.

▶ Low-level audio and visual features are extracted.▶ Mid-level audio features are generated based on the low-

level ones.

▶ The audio and visual features are then fused at the feature-level and a two-class SVM is trained.

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Detection of Violent Scenes using Affective Features

Audio Features - 1

▶ Affect-related audio features used in the work are: Audio energy

related to the arousal aspect. high/low energy corresponds to high/low emotion intensity. used for vocal emotion detection.

Mel-Frequency Cepstral Coefficients (MFCC) related to the arousal aspect. works well for the detection of excitement/non-excitement.

Pitch related to the valence aspect. significant for emotion detection in speech and music.

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Detection of Violent Scenes using Affective Features

Audio Features - 2

▶ Each video shot has different numbers of audio energy, pitch and MFCC feature vectors (due to varying shot durations).

▶ Audio representations are obtained by computing mean and standard deviation for these audio features.

▶ Abstraction for MFCC: MFCC-based Bag of Audio Words (BoAW) approach is chosen to

generate mid-level audio representations. Two different audio vocabularies are constructed: violence and

non-violence vocabularies (by k-means clustering). MFCC of violent/non-violent movie segments are used to

construct violence/non-violence words. Violence and non-violence word occurrences within a video

shot are represented by a BoAW histogram.

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Detection of Violent Scenes using Affective Features

Visual Features

▶ Average motion related to the arousal aspect. Motion vectors are computed using block-based motion

estimation. Average motion is found as the average magnitude of all

motion vectors.

▶ We compute average motion around the keyframe of video shots.

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Detection of Violent Scenes using Affective Features

Results & Discussion - 1

▶ The performance of our method was assessed on 3 Hollywood movies (evaluation criteria: MAP at 100).

▶ We submitted five runs: r1-low-level: low-level audio and visual features, Runs based on mid-level audio and low-level visual features

r2-mid-level-100k: 100k samples for dictionary construction, r3-mid-level-300k: 300k samples for dictionary construction, r4-mid-level-300k-default: 300k samples for dictionary

construction + SVM default parameters, and r5-mid-level-500k: 500k samples for dictionary construction.

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Detection of Violent Scenes using Affective Features

Results & Discussion - 2

▶ Slightly better performance is achieved with mid-level representations compared to the low-level one.

▶ Using affect-related features to describe violence needs some improvements (especially the visual part).

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Run AED-P AED-R AED-Fr1-low-level 0.141 0.597 0.2287

r2-mid-level-100k 0.140 0.629 0.2285

r3-mid-level-300k 0.144 0.625 0.2337

r4-mid-level-300k-default 0.190 0.627 0.2971

r5-mid-level-500k 0.154 0.603 0.2457

Table 1 – Precision, Recall and F-measure at shot level

Run MAP at 20 MAP at 100r1-low-level 0.2132 0.18502

r2-mid-level-100k 0.2037 0.14492

r3-mid-level-300k 0.3593 0.18538

r4-mid-level-300k-default 0.1547 0.15083

r5-mid-level-500k 0.15 0.11527

Table 2 – Mean Average Precision (MAP) values at 20 and 100

Detection of Violent Scenes using Affective Features

Conclusions & Future Work

▶ The aim of this work was to investigate whether affect-related features are well-suited to describe violence.

▶ Affect-related audio and visual features are merged in a supervised manner using SVM.

▶ Our main finding is that more sophisticated affect-related features are necessary to describe the content of videos (especially the visual part).

▶ Our next step in this work is to use mid-level features such as human facial features, and more sophisticated motion descriptors such as Lagrangian

measuresfor video content representation.

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Detection of Violent Scenes using Affective Features

Thank you!

Questions?

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