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Motion Retrieval Using Low‐Rank Subspace Decomposition of Motion Volume
Author(s) -
Sun Chuan,
Junejo Imran,
Foroosh Hassan
Publication year - 2011
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/j.1467-8659.2011.02048.x
Subject(s) - computer science , dimensionality reduction , subspace topology , motion (physics) , artificial intelligence , curse of dimensionality , motion estimation , sequence (biology) , pattern recognition (psychology) , similarity (geometry) , representation (politics) , rank (graph theory) , discriminative model , structure from motion , motion capture , nonlinear dimensionality reduction , mathematics , image (mathematics) , genetics , combinatorics , politics , political science , law , biology
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.