Boosting object detection using feature selection

Feature subset selection has received considerable attention in the machine learning literature, however, it has not been fully explored or exploited in the computer vision area. In this paper, we consider the problem of object detection using Genetic Algorithms (GAs) for feature subset selection. We argue that feature selection is an important problem in object detection, and demonstrate that GAs provide a simple, general, and powerful framework for selecting good sets of features, leading to lower detection error rates. As a case study, we have chosen to perform feature extraction using the popular method of Principal Component Analysis (PCA) and classification using Support Vector Machines (SVMs). We have tested this framework on two difficult and practical object detection problems: vehicle detection and face detection. Experimental results demonstrate significant performance improvements in both cases.