Object-Centric Spatial Pooling for Image Classification

  • Olga Russakovsky
  • Yuanqing Lin
  • Kai Yu
  • Li Fei-Fei
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7573)

Abstract

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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Olga Russakovsky
    • 1
  • Yuanqing Lin
    • 2
  • Kai Yu
    • 3
  • Li Fei-Fei
    • 1
  1. 1.Stanford UniversityUSA
  2. 2.NEC Laboratories AmericaUSA
  3. 3.Baidu Inc.China

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