Abstract: Edge-fragment feature is very stable in detecting objects with large variances in color, texture, and shape. Traditional methods that extract edge-fragments from a few manually segmented samples cannot meet the requirement of statistical learning in case of large number of training samples. However, if the training samples are automatically segmented, it is inevitable that huge amount of edge-fragments from background of the training samples will appear in the feature set. In that case, the feature-selection algorithm is very critical to the detection task. In this paper, a feature-selection algorithm based on weight updating scheme of Adaboost and K-L distance is proposed. In each round of Adaboost learning, a subset of all the edge-fragments is selected as the feature set for training Adaboost weak classifier. Because the proposed feature-selection algorithm takes into account the edge-fragments' discrimination information between positive samples and negative samples, it can effectively reduce the number of edge-fragments from background in the final classifier. Experimental results show that the proposed algorithm is effective.