Supervised Learning for Object Classification from Image and RFID Data
Yohei Shirasaka, Takehisa Yairi, Hirofumi Kanazaki, Junichi Shibata, Kazuo Machida · 2006 SICE-ICASE International Joint Conference · 2006
Position estimation and tracking of multiple objects by vision sensors is one of the most fundamental technologies. While the vision sensors provide high accuracy measurements for position estimation, they require suitable features of objects for accurate recognition and detection as prior knowledge. Especially, learning of appearance based features of objects requires large quantities of training data, which makes development costs. This paper proposes a method for learning appearance based features of objects using auxiliary data of RFID. In this method, the RFID device is used as a supervisor to semi-automatically construct the training data set for each object. Since it is difficult to observe what ID does an object image correspond to, this problem comes down to supervised learning using incompletely labeled features. This paper proposes a learning method using Kernel PCA and EM algorithm, and verifies the effectiveness and robustness of this method