Out-of-core tensor approximation of multi-dimensional matrices of visual data

Hongcheng Wang, Qing Wu, Lin Shi, Yizhou Yu, Narendra Ahuja

Research output: Contribution to journalConference article

Abstract

Tensor approximation is necessary to obtain compact multilinear models for multi-dimensional visual datasets. Traditionally, each multi-dimensional data item is represented as a vector. Such a scheme flattens the data and partially destroys the internal structures established throughout the multiple dimensions. In this paper, we retain the original dimensionality of the data items to more effectively exploit existing spatial redundancy and allow more efficient computation. Since the size of visual datasets can easily exceed the memory capacity of a single machine, we also present an out-of-core algorithm for higher-order tensor approximation. The basic idea is to partition a tensor into smaller blocks and perform tensor-related operations blockwise. We have successfully applied our techniques to three graphics-related data-driven models, including 6D bidirectional texture functions, 7D dynamic BTFs and 4D volume simulation sequences. Experimental results indicate that our techniques can not only process out-of-core data, but also achieve higher compression ratios and quality than previous methods.

Original languageEnglish (US)
Pages (from-to)527-535
Number of pages9
JournalACM Transactions on Graphics
Volume24
Issue number3
DOIs
StatePublished - Jul 1 2005
EventACM SIGGRAPH 2005 - Los Angeles, CA, United States
Duration: Jul 31 2005Aug 4 2005

Keywords

  • Bidirectional Texture Functions
  • Block-Based Partitioning
  • Multilinear Models
  • Spatial Coherence
  • Volume Simulations

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design

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