Learn More
— In this paper a novel framework for multimodal categorization using Bag of multimodal LDA models is proposed. The main issue, which is tackled in this paper, is granularity of categories. The categories are not fixed but varied according to context. Selective attention is the key to model this granularity of categories. This fact motivates us to introduce(More)
— In this paper, we propose a nonparametric Bayesian framework for categorizing multimodal sensory signals such as audio, visual, and haptic information by robots. The robot uses its physical embodiment to grasp and observe an object from various viewpoints as well as listen to the sound during the observation. The multimodal information enables the robot(More)
— In this paper we propose LDA-based framework for multimodal categorization and words grounding for robots. The robot uses its physical embodiment to grasp and observe an object from various view points as well as listen to the sound during the observing period. This multimodal information is used for categorizing and forming multimodal concepts. At the(More)