Weak Hypotheses and Boosting for Generic Object Detection and Recognition

In this paper we describe the first stage of a new learning system for object detection and recognition. For our system we propose Boosting [5] as the underlying learning technique. This allows the use of very diverse sets of visual features in the learning process within a common framework: Boosting -- together with a weak hypotheses finder -may choose very inhomogeneous features as most relevant for combination into a final hypothesis. As another advantage the weak hypotheses finder may search the weak hypotheses space without explicit calculation of all available hypotheses, reducing computation time. This contrasts the related work of Agarwal and Roth [1] where Winnow was used as learning algorithm and all weak hypotheses were calculated explicitly. In our first empirical evaluation we use four types of local descriptors: two basic ones consisting of a set of grayvalues and intensity moments and two high level descriptors: moment invariants [8] and SIFTs [12]. The descriptors are c...

• Type: Conference
• Year: 2004
• Where: ECCV
• Authors: Andreas Opelt, Michael Fussenegger, Axel Pinz, Peter Auer
• Keywords: Available Hypotheses, Computer Vision, ECCV 2004, Generic Object Recognition, Simple Rectangular Hypotheses, Weak Hypotheses, Weak Hypotheses Space