Transfer Learning for Endoscopic Image Classification

Autor:	Sonoyama, Shoji, Tamaki, Toru, Hirakawa, Tsubasa, Raytchev, Bisser, Kaneda, Kazufumi, Koide, Tetsushi, Yoshida, Shigeto, Mieno, Hiroshi, Tanaka, Shinji
Rok vydání:	2016
Předmět:	Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	In this paper we propose a method for transfer learning of endoscopic images. For transferring between features obtained from images taken by different (old and new) endoscopes, we extend the Max-Margin Domain Transfer (MMDT) proposed by Hoffman et al. in order to use L2 distance constraints as regularization, called Max-Margin Domain Transfer with L2 Distance Constraints (MMDTL2). Furthermore, we develop the dual formulation of the optimization problem in order to reduce the computation cost. Experimental results demonstrate that the proposed MMDTL2 outperforms MMDT for real data sets taken by different endoscopes. Comment: 5 pages, FCV2016
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1608.06713 Zobrazit plný text záznamu View this record from Arxiv