title: Fast-learning adaptive-subspace self-organizing map: an application to saliency-based invariant image feature construction.
abstract: The adaptive-subspace self-organizing map (ASSOM) is useful for invariant feature generation and visualization. However, the learning procedure of the ASSOM is slow. In this paper, two fast implementations of the ASSOM are proposed to boost ASSOM learning based on insightful discussions of the basis rotation operator of ASSOM. We investigate the objective function approximately maximized by the classical rotation operator. We then explore a sequence of two schemes to apply the proposed ASSOM implementations to saliency-based invariant feature construction for image classification. In the first scheme, a cumulative activity map computed from a single ASSOM is used as descriptor of the input image. In the second scheme, we use one ASSOM for each image category and a joint cumulative activity map is calculated as the descriptor. Both schemes are evaluated on a subset of the Corel photo database with ten classes. The multi-ASSOM scheme is favored. It is also applied to adult image filtering and shows promising results.
authors.affiliation: Media Processing and Communication Lab, Department of Electronics and Communication Engineering, Sun Yat-sen University, 510275 Guangzhou, China. zhenghch@mail.sysu.edu.cn
authors.forename: Huicheng
authors.initials: H
authors.lastname: Zheng
authors.forename: Grégoire
authors.initials: G
authors.lastname: Lefebvre
authors.forename: Christophe
authors.initials: C
authors.lastname: Laurent
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D016014:Linear Models; D015203:Reproducibility of Results; D012867:Skin