title: Transfer learning for Bag-of-Visual words approach to NBI endoscopic image classification.
abstract: We address a problem of endoscopic image classification taken by different (e.g., old and new) endoscopies. Our proposed method formulates the problem as a constraint optimization that estimates a linear transformation between feature vectors (or Bag-of-Visual words histograms) in a framework of transfer learning. Experimental results show that the proposed method works much better than the case without feature transformation.
authors.forename: Shoji
authors.initials: S
authors.lastname: Sonoyama
authors.forename: Tsubasa
authors.initials: T
authors.lastname: Hirakawa
authors.forename: Toru
authors.initials: T
authors.lastname: Tamaki
authors.forename: Takio
authors.initials: T
authors.lastname: Kurita
authors.forename: Bisser
authors.initials: B
authors.lastname: Raytchev
authors.forename: Kazufumi
authors.initials: K
authors.lastname: Kaneda
authors.forename: Tetsushi
authors.initials: T
authors.lastname: Koide
authors.forename: Shigeto
authors.initials: S
authors.lastname: Yoshida
authors.forename: Yoko
authors.initials: Y
authors.lastname: Kominami
authors.forename: Shinji
authors.initials: S
authors.lastname: Tanaka
mesh_terms: D004724:Endoscopy; D007090:Image Interpretation, Computer-Assisted