The presentation of our method at the MediaEval workshop of 2014.

1. TUB-IRML at MediaEval 2014 Violent Scenes Detection
Task: Violence Modeling through Feature Space Partitioning
Esra Acar, Sahin Albayrak
Competence Center Information Retrieval & Machine Learning

2. Outline
►The Violence Detection Method
Video Representation
 Violence Detection Model
►Results & Discussion
►Conclusions & Future Work
16 October 2014 TUB-IRML at MediaEval 2014 Violent Scenes Detection Task 2

3. The Violence Detection Method
►The two main components of our method are:
 (1) the representation of video segments, and
 (2) the learning of a violence model.
16 October 2014 TUB-IRML at MediaEval 2014 Violent Scenes Detection Task 3

4. Video Representation (1)
The generation process of sparse coding based audio and visual representations for video segments.
16 October 2014 TUB-IRML at MediaEval 2014 Violent Scenes Detection Task 4

5. Video Representation (2)
The generation of audio and visual dictionaries with sparse coding.
16 October 2014 TUB-IRML at MediaEval 2014 Violent Scenes Detection Task 5

6. Video Representation (3)
► In addition to the mid-level audio and visual representations,
we use low-level features which are:
Motion-related descriptors – Violent Flow (ViF) which is a
descriptor proposed for real-time detection of violent crowd
behaviors, and
 Static content representations – Affect-related color
descriptors such as statistics on saturation, brightness and
hue in the HSL color space, and colorfulness.
16 October 2014 TUB-IRML at MediaEval 2014 Violent Scenes Detection Task 6

7. Violence Detection Model
►Violence is a concept which can audio-visually be expressed in
diverse manners.
►We learn multiple models for the violence concept instead of a
unique model.
 Feature space partitioning by clustering video segments in
the training dataset, and
 Learn a different model for each violence sub-concept.
►We perform a classifier selection to solve the classifier
combination issue.
16 October 2014 TUB-IRML at MediaEval 2014 Violent Scenes Detection Task 7

8. Results & Discussion
The MAP2014 and MAP@100 of our method with different representations
Method MAP2014 –
Movies
MAP@100 –
Movies
MAP2014 –
Web videos
MAP@100 –
Web videos
Run1 0.169 0.368 0.517 0.582
Run2 0.139 0.284 0.371 0.478
Run3 0.080 0.208 0.477 0.495
Run4 0.172 0.409 0.489 0.586
Run5 0.170 0.406 0.479 0.567
SVM-based
0.093 0.302 - -
unique model
Run1  MFCC-based mid-level audio representations
Run2  HoG- and HoF-based mid-level features and ViF
Run3  Affect-related color features
Run4  Audio and visual features (except color)
Run5  All audio-visual representations are linearly fused at the decision level
16 October 2014 TUB-IRML at MediaEval 2014 Violent Scenes Detection Task 8

9. Conclusions & Future Work
►The mid-level audio representation based on MFCC and
sparse coding
 provides promising performance in terms of MAP2014 and
MAP@100 metrics, and
 also outperforms our visual representations.
► As a future work, we need to
 extend/improve our visual representation set, and
 further investigate the feature space partitioning concept.
16 October 2014 TUB-IRML at MediaEval 2014 Violent Scenes Detection Task 10

10. M.Sc.
Competence Center Information Retrieval &
Machine Learning
www.dai-labor.de
Fon
Fax
+49 (0) 30 / 314 – 74
+49 (0) 30 / 314 – 74 003
DAI-Labor
Technische Universität Berlin
Fakultät IV – Elektrontechnik & Informatik
Sekretariat TEL 14
Ernst-Reuter-Platz 7
10587 Berlin, Deutschland
11
Esra Acar
Researcher
esra.acar@tu-berlin.de
Thanks!
013
TUB-IRML at MediaEval 16 October 2014 2014 Violent Scenes Detection Task