A Multiple Classifier System Approach to Affine Invariant Object Recognition

We propose an affine invariant object recognition system which is based on the principle of multiple classifier fusion. Accordingly, two recognition experts are developed and used in tandem. The first expert performs a course grouping of the object hypotheses based on an entropy criterion. This initial classification is performed using colour cues. The second expert establishes the object identity by considering only the subset of candidate models contained in the most probable coarse group. This expert takes into account geometric relations between object primitives and determines the winning hypothesis by means of relaxation labelling. We demonstrate the effectiveness of the proposed object recognition strategy on the Surrey Object Image Library database. The experimental results not only show improved recognition performance but also a computational speed up.

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