Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Edge detector evaluation using empirical ROC curves
Computer Vision and Image Understanding - Special issue on empirical evaluation of computer vision algorithms
Statistical Edge Detection: Learning and Evaluating Edge Cues
IEEE Transactions on Pattern Analysis and Machine Intelligence
An introduction to variable and feature selection
The Journal of Machine Learning Research
Grafting: fast, incremental feature selection by gradient descent in function space
The Journal of Machine Learning Research
Use of the zero norm with linear models and kernel methods
The Journal of Machine Learning Research
Learning to Detect Natural Image Boundaries Using Local Brightness, Color, and Texture Cues
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multirelational classification: a multiple view approach
Knowledge and Information Systems
Online feature selection for mining big data
Proceedings of the 1st International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications
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Online feature selection (OFS) provides an efficient way to sort through a large space of features, particularly in a scenario where the feature space is large and features take a significant amount of memory to store. Image processing operators, and especially combinations of image processing operators, provide a rich space of potential features for use in machine learning for image processing tasks but they are expensive to generate and store. In this paper we apply OFS to the problem of edge detection in grayscale imagery. We use a standard data set and compare our results to those obtained with traditional edge detectors, as well as with results obtained more recently using "statistical edge detection." We compare several different OFS approaches, including hill climbing, best first search, and grafting.