Decision combination of multiple classifiers for pattern classification: hybridisation of majority voting and divide and conquer techniques

In many applications of computer vision, combination of decisions from multiple sources is a very important way of achieving more accurate and robust classification. Many such techniques can be used, two of which are the Majority Voting and the Divide and Conquer techniques. The former achieves decision combination by measuring consensus among the participating classifiers and the latter achieves the same by dividing the problem into smaller problems and solving each of these sub-problems more efficiently. Both these approaches have their advantages and disadvantages. In this paper, a novel approach to combining these two techniques is presented. Although the success of the approach has been demonstrated in a typical application area of computer vision (recognition of complex and highly variable image data), the approach is completely generalised and is applicable to other task domains.