Novel object recognition based on hypothesis generation and verification

In this paper, a novel two-stage object recognition algorithm is proposed. As an iterative line search optimization method, the mean shift technique is used for fast generalizing of a set of hypothesis. During the hypothesis generalization procedure, the weighted global shape context is integrated with weighted gray histogram to enhance object representation. As a measure for the discriminative power of probability distributions, the symmetric discrete KL divergence is adopted instead of Bhattacharyya coefficient. In order to handle the problem of negative weights for samples, a new weight regulation method is introduced. For the verification stage, a robust circular Gabor-based object matching algorithm using weighted Hausdorff distance is adopted to give final verification for the set of hypothesis.