Hierarchical multilevel object recognition using Markov model

In this study, we address the issue on multilevel object recognition. The multilevel object recognition is object recognition in various levels, that is, simultaneous recognition of its instance, category, material, etc. At each level, many recognition methods have been proposed in the literature. Therefore it is straightforward to design a multilevel object recognition system using conventional methods independently. However, these “levels” are related each other and form hierarchical structure. Hence the recognition performance can be improved by considering consistency of the recognition results at all levels. To model the consistency, we formulate the problem as finding the Viterbi path in a Markov model, since the consistent recognition results can be thought of as the most likely sequence of the states. We implemented the proposed multilevel object recognition system and evaluated it to show validity.