Proceedings ArticleDOI
Combining multiple evidence for video classification
S. Vakkalanka,C. Krishna Mohan,R. Kumaraswamy,B. Yegnanarayana +3 more
- pp 187-192
Reads0
Chats0
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
More filters
Proceedings ArticleDOI
Multimedia genre characterisation with fuzzy embedding classifiers
TL;DR: In this article, a feature extraction architecture and a novel learning algorithm for multimedia genre characterisation is presented, which takes into account structural and cognitive content descriptors, rather than low-level features.
Proceedings ArticleDOI
Multimodal Genre Analysis Applied to Digital Television Archives
TL;DR: A method to classify the genre of TV programmes is presented, using four multimodal vectors, including both low-level perceptual descriptors and higher-level, human-centred features, to describe semantic properties of multimedia data.
Journal ArticleDOI
Audio_Video Based Segmentation and Classification Using SVM
TL;DR: Experimental results of audio classification evidence and video are combined using weighted sum rule for audio-video based segmentation and classification.
Book
Video Classification using Spatio Temporal Features
Rajib Das,M. Kalaiselvi Geetha +1 more
TL;DR: An edge, histogram & motion based approach is proposed to classify video into prespecified genres and shows that the proposed method gives good accuracy on a set of five different video genres.
Multimedia genre characterisation with fuzzy embedding classifiers
TL;DR: This paper presents a feature extraction architecture and a novel learning algorithm for multimedia genre characterisation, based on fuzzy set theory and makes use of fuzzy C-means algorithm as the kernel to learn concepts configurations from data.
References
More filters
Journal ArticleDOI
On combining classifiers
TL;DR: A common theoretical framework for combining classifiers which use distinct pattern representations is developed and it is shown that many existing schemes can be considered as special cases of compound classification where all the pattern representations are used jointly to make a decision.
Journal ArticleDOI
An introduction to hidden Markov models
TL;DR: The purpose of this tutorial paper is to give an introduction to the theory of Markov models, and to illustrate how they have been applied to problems in speech recognition.
Journal ArticleDOI
Methods of combining multiple classifiers and their applications to handwriting recognition
TL;DR: On applying these methods to combine several classifiers for recognizing totally unconstrained handwritten numerals, the experimental results show that the performance of individual classifiers can be improved significantly.
Proceedings ArticleDOI
Comparing images using color coherence vectors
TL;DR: It is shown that CCV’s can give superior results to color histogram-based methods for comparing images that incorporates spatial information, and to whom correspondence should be addressed tograms for image retrieval.
Support Vector Machines for Large-Scale Regression Problems
Ronan Collobert,Samy Bengio +1 more
TL;DR: In this paper, learning reference EPFL-REPORT-82604 is used to learn Reference EPFL this paper. But learning reference is not considered in this paper. http://publications.idiap.ch/downloads/reports/2000/rr00-17.pdf Record created on 2006-03-10, modified on 2017-05-10