: This paper presents a feature selection technique based on distributional differences for efficient machine learning. Initial training data consists of data including many featur...
This paper describes an object detection framework that learns the discriminative co-occurrence of multiple features. Feature co-occurrences are automatically found by Sequential F...
Boosting has been widely applied in computer vision, especially after Viola and Jones's seminal work [23]. The marriage of rectangular features and integral-imageenabled fast...
The problem of selecting a subset of relevant features in a potentially overwhelming quantity of data is classic and found in many branches of science. Examples in computer vision...
Feature selection is an important task in order to achieve better generalizability in high dimensional learning, and structure learning of Markov random fields (MRFs) can automat...