Unsupervised learning of visual structure

Shimon Edelman, Nathan Intrator, Judah S. Jacobson

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


To learn a visual code in an unsupervised manner, one may attempt to capture those features of the stimulus set that would contribute significantly to a statistically efficient representation (as dictated, e.g., by the Minimum Description Length principle). Paradoxically, all the candidate features in this approach need to be known before statistics over them can be computed. This paradox may be circumvented by confining the repertoire of candidate features to actual scene fragments, which resemble the “what+where” receptive fields found in the ventral visual stream in primates. We describe a single-layer network that learns such fragments from unsegmented raw images of structured objects. The learning method combines fast imprinting in the feedforward stream with lateral interactions to achieve single-epoch unsupervised acquisition of spatially localized features that can support systematic treatment of structured objects [1].

Original languageEnglish
Title of host publicationBiologically Motivated Computer Vision - 2nd International Workshop, BMCV 2002, Proceedings
EditorsHeinrich H. Bulthoff, Christian Wallraven, Seong-Whan Lee, Tomaso A. Poggio
PublisherSpringer Verlag
Number of pages14
ISBN (Electronic)9783540001744
StatePublished - 2002
Event2nd International Workshop on Biologically Motivated Computer Vision, BMCV 2002 - Tubingen, Germany
Duration: 22 Nov 200224 Nov 2002

Publication series

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


Conference2nd International Workshop on Biologically Motivated Computer Vision, BMCV 2002


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