Motion Retrieval Using Low-Rank Subspace Decomposition of Motion Volume

    Authors

    C. Sun; I. Junejo;H. Foroosh

    Comments

    Authors: contact us about adding a copy of your work at STARS@ucf.edu

    Abbreviated Journal Title

    Comput. Graph. Forum

    Keywords

    ORDER TENSORS; CAPTURE DATA; APPROXIMATION; Computer Science, Software Engineering

    Abstract

    This paper proposes a novel framework that allows for a flexible and an efficient retrieval of motion capture data in huge databases. The method first converts an action sequence into a novel representation, i.e. the Self-Similarity Matrix (SSM), which is based on the notion of self-similarity. This conversion of the motion sequences into compact and low-rank subspace representations greatly reduces the spatiotemporal dimensionality of the sequences. The SSMs are then used to construct order-3 tensors, and we propose a low-rank decomposition scheme that allows for converting the motion sequence volumes into compact lower dimensional representations, without losing the nonlinear dynamics of the motion manifold. Thus, unlike existing linear dimensionality reduction methods that distort the motion manifold and lose very critical and discriminative components, the proposed method performs well even when inter-class differences are small or intra-class differences are large. In addition, the method allows for an efficient retrieval and does not require the time-alignment of the motion sequences. We evaluate the performance of our retrieval framework on the CMU mocap dataset under two experimental settings, both demonstrating promising retrieval rates.

    Journal Title

    Computer Graphics Forum

    Volume

    30

    Issue/Number

    7

    Publication Date

    1-1-2011

    Document Type

    Article

    Language

    English

    First Page

    1953

    Last Page

    1962

    WOS Identifier

    WOS:000296915400010

    ISSN

    0167-7055

    Share

    COinS