title: An Efficient Bayesian Approach to Exploit the Context of Object-Action Interaction for Object Recognition.
abstract: This research features object recognition that exploits the context of object-action interaction to enhance the recognition performance. Since objects have specific usages, and human actions corresponding to these usages can be associated with these objects, human actions can provide effective information for object recognition. When objects from different categories have similar appearances, the human action associated with each object can be very effective in resolving ambiguities related to recognizing these objects. We propose an efficient method that integrates human interaction with objects into a form of object recognition. We represent human actions by concatenating poselet vectors computed from key frames and learn the probabilities of objects and actions using random forest and multi-class AdaBoost algorithms. Our experimental results show that poselet representation of human actions is quite effective in integrating human action information into object recognition.
authors.affiliation: School of Electronic and Electrical Engineering, Sungkyunkwan University, Suwon 16419, Korea. beagii@skku.edu.
authors.forename: Sungbaek
authors.initials: S
authors.lastname: Yoon
authors.affiliation: School of Electronic and Electrical Engineering, Sungkyunkwan University, Suwon 16419, Korea. hyunjinp@skku.edu.
authors.forename: Hyunjin
authors.initials: H
authors.lastname: Park
authors.affiliation: School of Electronic and Electrical Engineering, Sungkyunkwan University, Suwon 16419, Korea. jhyi@skku.edu.|School of Information and Communication Engineering, North University of China, Taiyuan 03000, China. jhyi@skku.edu.
authors.forename: Juneho
authors.initials: J
authors.lastname: Yi
keywords: object recognition; object-action context; object-human interaction