Comparison of action-grounded and non-action-grounded 3-D shape features for object affordance classification.
Barry RidgeEmre UgurAles UdePublished in: ICAR (2015)
Keyphrases
- feature vectors
- classification accuracy
- feature set
- action rules
- feature extraction
- feature space
- d objects
- classification process
- classification method
- extracting features
- classification models
- object model
- decision trees
- benchmark datasets
- feature selection
- target object
- extracted features
- feature analysis
- global shape
- class labels
- svm classifier
- support vector machine svm
- multi view
- moving objects
- spatial information
- feature subset
- machine learning
- single image
- feature selection algorithms
- feature values
- image classification
- supervised learning
- support vector machine
- category labels