Image classification using super-vector coding of local image descriptors

Xi Zhou, Kai Yu, Tong Zhang, Thomas S. Huang

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

Abstract

This paper introduces a new framework for image classification using local visual descriptors. The pipeline first performs a nonlinear feature transformation on descriptors, then aggregates the results together to form image-level representations, and finally applies a classification model. For all the three steps we suggest novel solutions which make our approach appealing in theory, more scalable in computation, and transparent in classification. Our experiments demonstrate that the proposed classification method achieves state-of-the-art accuracy on the well-known PASCAL benchmarks.

Original languageEnglish (US)
Title of host publicationComputer Vision, ECCV 2010 - 11th European Conference on Computer Vision, Proceedings
PublisherSpringer-Verlag Berlin Heidelberg
Pages141-154
Number of pages14
EditionPART 5
ISBN (Print)3642155545, 9783642155543
DOIs
StatePublished - 2010
Event11th European Conference on Computer Vision, ECCV 2010 - Heraklion, Crete, Greece
Duration: Sep 10 2010Sep 11 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 5
Volume6315 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference11th European Conference on Computer Vision, ECCV 2010
CountryGreece
CityHeraklion, Crete
Period9/10/109/11/10

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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