A new fuzzy shape context approach based on multi-clue and state reservoir computing

This paper first builds a rule-based fuzzy representation of shape context and then present a multi-clue based fuzzy shape context approach (MFSC) using combination of geometric information and graph transduction. The MFSC takes complexity of object shape into account. In this approach, the distance between arbitrary two sampled points on any shape is redefined and graph transduction is used to correct and compensate training error. Furthermore, we propose a new fuzzy shape context approach based on both multi-clue and state reservoir computing. The experimental results show that the accuracy of detection achieved by our new approach on Kimia-216 and Kimia-99 datasets reaches up to 99.35% and 98.56%, respectively, which outperforms that of all the state-of-the-art shape context approaches.