Sketch-based human motion retrieval via selected 2D geometric posture descriptor

Authors: Xiao, J., Tang, Z., Feng, Y. and Xiao, Z.

Journal: Signal Processing

Volume: 113

Pages: 1-8

ISSN: 0165-1684

DOI: 10.1016/j.sigpro.2015.01.004

Abstract:

Sketch-based human motion retrieval is a hot topic in computer animation in recent years. In this paper, we present a novel sketch-based human motion retrieval method via selected 2-dimensional (2D) Geometric Posture Descriptor (2GPD). Specially, we firstly propose a rich 2D pose feature call 2D Geometric Posture Descriptor (2GPD), which is effective in encoding the 2D posture similarity by exploiting the geometric relationships among different human body parts. Since the original 2GPD is of high dimension and redundant, a semi-supervised feature selection algorithm derived from Laplacian Score is then adopted to select the most discriminative feature component of 2GPD as feature representation, and we call it as selected 2GPD. Finally, a posture-by-posture motion retrieval algorithm is used to retrieve a motion sequence by sketching several key postures. Experimental results on CMU human motion database demonstrate the effectiveness of our proposed approach.

https://eprints.bournemouth.ac.uk/21647/

Source: Scopus