Proceedings ArticleDOI
Combining multiple evidence for video classification
S. Vakkalanka,C. Krishna Mohan,R. Kumaraswamy,B. Yegnanarayana +3 more
- pp 187-192
TLDR
The efficacy of the performance based fusion method is demonstrated by applying it to classification of short video clips into six popular TV broadcast genre, namely cartoon, commercial, news, cricket, football, and tennis.Abstract:
In this paper, we investigate the problem of video classification into predefined genre, by combining the evidence from multiple classifiers. It is well known in the pattern recognition community that the accuracy of classification obtained by combining decisions made by independent classifiers can be substantially higher than the accuracy of the individual classifiers. The conventional method for combining individual classifiers weighs each classifier equally (sum or vote rule fusion). In this paper, we study a method that estimates the performances of the individual classifiers and combines the individual classifiers by weighing them according to their estimated performance. We demonstrate the efficacy of the performance based fusion method by applying it to classification of short video clips (20 seconds) into six popular TV broadcast genre, namely cartoon, commercial, news, cricket, football, and tennis. The individual classifiers are trained using different spatial and temporal features derived from the video sequences, and two different classifier methodologies, namely hidden Markov models (HMMs) and support vector machines (SVMs). The experiments were carried out on more than 3 hours of video data. A classification rate of 93.12% for all the six classes and 97.14% for sports category alone has been achieved, which is significantly higher than the performance of the individual classifiers.read more
Citations
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A study on video semantics; overview, challenges, and applications
Proceedings ArticleDOI
Feature extraction and statistical analysis of videos for cinemetric applications
TL;DR: The developed framework analyses the available video content and extracts characteristics related to color, motion, contrast, shot length, tempo, face to frame ratios etc in MPEG 7 AVDP profile format.
Proceedings ArticleDOI
Fuzzy mining of multimedia genre applied to television archives
A. Messina,M. Montagnuolo +1 more
TL;DR: A novel fuzzy multimedia mining technique for genre characterisation, aimed at overcoming limitations of conventional crisp classification systems, is illustrated.
Journal ArticleDOI
Audio-video based Segmentation and Classification using SVM and AANN
K. Subashini,S. Palanivel +1 more
TL;DR: A method for combining audio and video for segmentation and classification using support vector machine (SVM) and autoassociative neural network (AANN) models is proposed.
Proceedings ArticleDOI
A GPU-assisted personal video organizing system
TL;DR: This paper presents a mechanism to help ordinary users organize their personal collection of videos based on categories they choose, and cluster the PHOG features extracted from selected key frames to form a representation for each user-selected category during the learning phase.
References
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