Fundamentals of digital image processing
Fundamentals of digital image processing
Elements of information theory
Elements of information theory
Speechreading using probabilistic models
Computer Vision and Image Understanding - Special issue on physics-based modeling and reasoning in computer vision
Relevance of time-frequency features for phonetic and speaker-channel classification
Speech Communication
Remote Sensing: Digital Image Analysis
Remote Sensing: Digital Image Analysis
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
Introduction to Algorithms
Assessing face and speech consistency for monologue detection in video
Proceedings of the tenth ACM international conference on Multimedia
Object Recognition with Informative Features and Linear Classification
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Fast Branch & Bound Algorithms for Optimal Feature Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast Binary Feature Selection with Conditional Mutual Information
The Journal of Machine Learning Research
IEEE Transactions on Pattern Analysis and Machine Intelligence
EURASIP Journal on Applied Signal Processing
Using Broad Phonetic Group Experts for Improved Speech Recognition
IEEE Transactions on Audio, Speech, and Language Processing
IEEE Transactions on Multimedia
Feature Selection for Gender Classification
IbPRIA'11 Proceedings of the 5th Iberian conference on Pattern recognition and image analysis
Expert Systems with Applications: An International Journal
Radar HRRP recognition based on discriminant information analysis
WSEAS Transactions on Information Science and Applications
Low bias histogram-based estimation of mutual information for feature selection
Pattern Recognition Letters
Lip peripheral motion for visual surveillance
Proceedings of the Fifth International Conference on Security of Information and Networks
Computers and Electrical Engineering
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The problem of feature selection has been thoroughly analyzed in the context of pattern classification, with the purpose of avoiding the curse of dimensionality. However, in the context of multimodal signal processing, this problem has been studied less. Our approach to feature extraction is based on information theory, with an application on multimodal classification, in particular audio-visual speech recognition. Contrary to previous work in information theoretic feature selection applied to multimodal signals, our proposed methods penalize features for their redundancy, achieving more compact feature sets and better performance. We propose two greedy selection algorithms, one that penalizes a proportion of feature redundancy, while the other uses conditional mutual information as an evaluation measure, for the selection of visual features for audio-visual speech recognition. Our features perform better than linear discriminant analysis, the most usual transform for dimensionality reduction in the field, across a wide range of dimensionality values and combined with audio at different quality levels.