Multibody grouping by inference of multiple subspaces from high-dimensional data using oriented-frames

Zhimin Fan*, Jie Zhou, Ying Wu

*Corresponding author for this work

Research output: Contribution to journalArticle

23 Scopus citations

Abstract

Recently, subspace constraints have been widely exploited in many computer vision problems such as multibody grouping. Under linear projection models, feature points associated with multiple bodies reside in multiple subspaces. Most existing factorization-based algorithms can segment objects undergoing independent motions. However, intersections among the correlated motion subspaces will lead most previous factorization-based algorithms to erroneous segmentation. To overcome this limitation, in this paper, we formulate the problem of multibody grouping as inference of multiple subspaces from a high-dimensional data space. A novel and robust algorithm is proposed to capture the configuration of the multiple subspace structure and to find the segmentation of objects by clustering the feature points into these inferred subspaces, no matter whether they are independent or correlated. In the proposed method, an Oriented-Frame (OF), which is a multidimensional coordinate frame, is associated with each data point indicating the point's preferred subspace configuration. Based on the similarity between the subspaces, novel mechanisms of subspace evolution and voting are developed. By filtering the outliers due to their structural incompatibility, the subspace configurations will emerge. Compared with most existing factorization-based algorithms that cannot correctly segment correlated motions, such as motions of articulated objects, the proposed method has a robust performance in both independent and correlated motion segmentation. A number of controlled and real experiments show the effectiveness of the proposed method. However, the current approach does not deal with transparent motions and motion subspaces of different dimensions.

Original languageEnglish (US)
Pages (from-to)91-104
Number of pages14
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Volume28
Issue number1
DOIs
StatePublished - Jan 1 2006

Keywords

  • Computer vision
  • Motion segmentation
  • Subspace constraints

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics

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