Semisupervised Feature Selection via Spline Regression for Video Semantic Recognition.
Yahong HanYi YangYan YanZhigang MaNicu SebeXiaofang ZhouPublished in: IEEE Trans. Neural Networks Learn. Syst. (2015)
Keyphrases
- feature selection
- regression algorithm
- feature extraction
- semi supervised
- human activities
- video event
- recognition rate
- recognition accuracy
- support vector
- video sequences
- model selection
- multimedia
- video streams
- supervised feature selection
- video content
- object recognition
- semantic concepts
- b spline
- regression model
- unsupervised learning
- video data
- machine learning
- text categorization
- space time
- event detection
- natural language
- regression methods
- sports video
- regression problems
- feature selection algorithms
- video analysis
- recognition algorithm
- semantic similarity
- text classification
- multi class
- mutual information
- video frames
- multimedia data
- high level
- low level
- semantic information
- action recognition
- multiresolution
- image classification
- semantic web
- semi supervised learning
- regression method
- real time
- visual concepts
- gaussian processes
- dimensionality reduction
- least squares
- classification accuracy
- activity recognition
- support vector machine