Categorization of multiple objects in a scene without semantic segmentation

Authors:
Lei Yang;Nanning Zheng;Mei Chen;Yang Yang;Jie Yang
Affiliations:
Xi An Jiaotong University, Shaan Xi, China;Xi An Jiaotong University, Shaan Xi, China;Intel Labs Pittsburgh, Pittsburgh;Xi An Jiaotong University, Shaan Xi, China;Carnegie Mellon University, Pittsburgh
Venue:
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part I
Year:
2009

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Abstract

In this paper, we present a novel approach for multi-object categorization within the Bag-of-Features (BoF) framework. We integrate a biased sampling component with a multi-instance multi-label leaning and classification algorithm into the categorization system. With the proposed approach, we addresses two issues in BoF related methods simultaneously: how to avoid scene modeling and how to predict labels of an image without explicitly semantic segmentation when multiple categories of objects are co-existing. The experimental results on VOC2007 dataset show that the proposed method outperforms others in the challenge’s classification task and achieves good performance in multi-object categorization tasks.