Object-centric spatial pooling for image classification

Olga Russakovsky, Yuanqing Lin, Kai Yu, Li Fei-Fei

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

122 Scopus citations


Spatial pyramid matching (SPM) based pooling has been the dominant choice for state-of-art image classification systems. In contrast, we propose a novel object-centric spatial pooling (OCP) approach, following the intuition that knowing the location of the object of interest can be useful for image classification. OCP consists of two steps: (1) inferring the location of the objects, and (2) using the location information to pool foreground and background features separately to form the image-level representation. Step (1) is particularly challenging in a typical classification setting where precise object location annotations are not available during training. To address this challenge, we propose a framework that learns object detectors using only image-level class labels, or so-called weak labels. We validate our approach on the challenging PASCAL07 dataset. Our learned detectors are comparable in accuracy with state-of-the-art weakly supervised detection methods. More importantly, the resulting OCP approach significantly outperforms SPM-based pooling in image classification.

Original languageEnglish (US)
Title of host publicationComputer Vision, ECCV 2012 - 12th European Conference on Computer Vision, Proceedings
Number of pages15
EditionPART 2
StatePublished - 2012
Externally publishedYes
Event12th European Conference on Computer Vision, ECCV 2012 - Florence, Italy
Duration: Oct 7 2012Oct 13 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume7573 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other12th European Conference on Computer Vision, ECCV 2012

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


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