Learning to recognize 3D objects with SNoW

Ming Hsuan Yang, Dan Roth, Narendra Ahuja

Research output: Chapter in Book/Report/Conference proceedingConference contribution

32 Citations (Scopus)

Abstract

This paper describes a novel view-based learning algorithm for 3D object recognition from 2D images using a network of linear units. The SNoW learning architecture is a sparse network of linear functions over a pre-defined or incrementally learned feature space and is specifically tailored for learning in the presence of a very large number of features. We use pixel-based and edge-based representations in large scale object recognition experiments in which the performance of SNoW is compared with that of Support Vector Machines (SVMs) and nearest neighbor using the 100 objects in the Columbia Image Object Database (COIL-100). Experimental results show that the SNoW-based method outperforms the SVM-based system in terms of recognition rate and the computational cost involved in learning. Most importantly, SNoW's performance degrades more gracefully when the training data contains fewer views. The empirical results also provide insight into practical and theoretical considerations on view-based methods for 3D object recognition.

Original languageEnglish
Title of host publicationComputer Vision - ECCV 2000 - 6th European Conference on Computer Vision, Proceedings
EditorsDavid Vernon
PublisherSpringer Verlag
Pages439-454
Number of pages16
ISBN (Print)3540676856
DOIs
Publication statusPublished - 2000
Event6th European Conference on Computer Vision, ECCV 2000 - Dublin, Ireland
Duration: 2000 Jun 262000 Jul 1

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume1842
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference6th European Conference on Computer Vision, ECCV 2000
CountryIreland
CityDublin
Period00/6/2600/7/1

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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