Analysis and classification of collective behavior using generative modeling and nonlinear manifold learning

JOURNAL OF THEORETICAL BIOLOGY

卷:336

Article

Butail, Sachit¹  Bollt, Erik M.²  Porfiri, Maurizio¹ 

[1] NYU, Polytech Inst, Dept Mech & Aerosp Engn, Brooklyn, NY 11201 USA

[2] Clarkson Univ, Dept Math & Comp Sci, Potsdam, NY 13699 USA

关键词: Classification;    Collective motion;    Fish schooling;    Generative modeling;    Isomap;   

DOI  :  10.1016/j.jtbi.2013.07.029

来源: Elsevier

【 摘 要 】

In this paper, we build a framework for the analysis and classification of collective behavior using methods from generative modeling and nonlinear manifold learning. We represent an animal group with a set of finite-sized particles and vary known features of the group structure and motion via a class of generative models to position each particle on a two-dimensional plane. Particle positions are then mapped onto training images that are processed to emphasize the features of interest and match attainable far-field videos of real animal groups. The training images serve as templates of recognizable patterns of collective behavior and are compactly represented in a low-dimensional space called embedding manifold. Two mappings from the manifold are derived: the manifold-to-image mapping serves to reconstruct new and unseen images of the group and the manifold-to-feature mapping allows frame-by-frame classification of raw video. We validate the combined framework on datasets of growing level of complexity. Specifically, we classify artificial images from the generative model, interacting self-propelled particle model, and raw overhead videos of schooling fish obtained from the literature. (C) 2013 Elsevier Ltd. All rights reserved.

【 授权许可】

Free