Recognizing objects on cluttered backgrounds

This paper deals with recognition of known 3-D objects in different orientations on cluttered backgrounds. As a recognition technique we apply support vector machines (SVMs). To cope with the cluttered background a tree structure of masks is introduced for each object. SVMs are then computed by masking the training sets with the appropriate masks. One- and two-class SVMs are combined in the recognition process. One-class SVMs, used at the first stage, allow us to avoid the “non-object” class generation usually required to classify unknown objects or other parts of a scene. Two-class SVMs are further applied to resolve the recognition process when necessary. The proposed method is compared with two other approaches and as demonstrated by experimental results it is robust to cluttered backgrounds. The advantage of the method is its ability to classify the pattern as unknown which has a valuable effect on false positive rate.