Object-Centric Spatial Pooling for Image Classification

Olga Russakovsky, Yuanqing Lin², Kai Yu³, and Li Fei-Fei

¹Stanford University, USA
olga@cs.stanford.edu
feifeili@cs.stanford.edu

²NEC Laboratories America, USA
ylin@nec-labs.com

³Baidu Inc., China
yukai@baidu.com

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.

LNCS 7573, p. 1 ff.

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