Name
Title | ||
|---|---|---|
Selecting features for object detection using an AdaBoost-compatible evaluation function |
Abstract | ||
|---|---|---|
This paper addresses the problem of selecting features in a visual object detection setup where a detection algorithm is applied to an input image represented by a set of features. The set of features to be employed in the test stage is prepared in two training-stage steps. In the first step, a feature extraction algorithm produces a (possibly large) initial set of features. In the second step, on which this paper focuses, the initial set is reduced using a selection procedure. The proposed selection procedure is based on a novel evaluation function that measures the utility of individual features for a certain detection task. Owing to its design, the evaluation function can be seamlessly embedded into an AdaBoost selection framework. The developed selection procedure is integrated with state-of-the-art feature extraction and object detection methods. The presented system was tested on five challenging detection setups. In three of them, a fairly high detection accuracy was effected by as few as six features selected out of several hundred initial candidates. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1016/j.patrec.2008.03.020 | Pattern Recognition Letters |
Keywords | Field | DocType |
detection method,object detection,certain detection task,adaboost,developed selection procedure,adaboost selection framework,proposed selection procedure,feature selection,adaboost-compatible evaluation function,selecting feature,challenging detection setup,high detection accuracy,initial set,detection algorithm,visual object detection setup,feature selection adaboost object detection,evaluation function,feature extraction | Computer vision,Signal processing,Object detection,AdaBoost,Feature selection,Compatibility (mechanics),Pattern recognition,Edge detection,Evaluation function,Feature extraction,Artificial intelligence,Mathematics | Journal |
Volume | Issue | ISSN |
29 | 11 | Pattern Recognition Letters |
Citations | PageRank | References |
1 | 0.36 | 21 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Luka Fürst | 1 | 19 | 2.72 |
| Sanja Fidler | 2 | 2087 | 116.71 |
| Aleš Leonardis | 3 | 1347 | 103.77 |