video genre classification of broadcasting material

Pascal KelmTechnische Universität Berlin

Communication Systems Group

Thursday, 2 April 2009

Video Genre Classification of Broadcasting Material

Video genre classification of broadcasting material

Real-time detection of different genres

2

Cartoon

News

Commercial Music

Sport

Introduction


Technical Overview

mpeg-2 Genre-recognition

image sequence

audio samples

feature-extraction

statistics

statistics

statistics

statistics

f Color-Descr.

f Texture-Descr

f Motion-Descr.

f Descriptor ...

w1

w4

mpeg-2 video

. . ...

I, P, B-frames, Preprocesseing

classification

feature-extraction

statistics

statistics

statistics

statistics

f Wave.-Descr.

f Power-Descr.

f Spectrum-D.

f Descriptor ...

w1

w4

classification

mpeg-2 audio

3

1. Input: MPEG-2 database (> 100 videos)

2. Multimodal descriptors (> 60)

3. Classification: Bayes, HMM, MLP, Decision Tree and SVM


Feature Domain 4

frame fLogo fScoreboardfSportField fAudience

original frame text localization (I-frame) ticker verification (P-frame)

Ticker DescriptorDEMO


Feature Domain 5

binarizationoriginal frame

ii TV, presenting more than hundred Dt channels at a tificationeProcess. We use a 3-stage process

n via analysis & classificatioi

text


Combining the AV-features via a set of Classifiers

(1)Single genre/ no-genre detector– e.g. Commercial VS Non-Commercial etc.

(2)Five-class inter-genre detector– Serial structure: classifier with the highest CA decides

first, whether the sequence should be assigned to ‘his genre’

– Parallel structure: all classifier decide simultaneously

6

DEMO

DEMO

Classification Domain


7

Genre Accuracy

Cartoon 93.38%

Commercial 98.53%

Music 94.65%

News 89.41%

Sport 98.5 %

Multi-Genre (Fusion) 89.1%

Multi-Genre (Fusion) 89.06%

• 50 sequences for training and 50 as testing-data• duration: 450 frames

Classification Domain


85-Genre Classification

video genre classification of broadcasting material

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