Efficient Object Localization with Variation-Normalized Gaussianized Vectors - Intelligent Video Event Analysis and Understanding - page 93

Efficient Object Localization
with Variation-Normalized Gaussianized Vectors
Xiaodan Zhuang, Xi Zhou, Mark A. Hasegawa-Johnson, and Thomas S. Huang
Abstract. Effective object localization relies on efficient and effective searching
method, and robust image representation and learning method. Recently, the Gaus-
sianized vector representation has been shown effective in several computer vision
applications, such as facial age estimation, image scene categorization and video
event recognition. However, all these tasks are classification and regression prob-
lems based on the whole images. It is not yet explored how this representation can be
efficiently applied in the object localization, which reveals the locations and sizes of
the objects. In this work, we present an efficient object localization approach for the
Gaussianized vector representation, following a branch-and-bound search scheme
introduced by Lampert et al. [5]. In particular, we design a quality bound for rectan-
gle sets characterized by the Gaussianized vector representation for fast hierarchical
search. This bound can be obtained for any rectangle set in the image, with little
extra computational cost, in addition to calculating the Gaussianized vector repre-
sentation for the whole image. Further, we propose incorporating a normalization
approach that suppresses the variation within the object class and the background
class. Experiments on a multi-scale car dataset show that the proposed object lo-
calization approach based on the Gaussianized vector representation outperforms
previous work using the histogram-of-keywords representation. The within-class
variation normalization approach further boosts the performance. This chapter is
an extended version of our paper at the 1st International Workshop on Interactive
Multimedia for Consumer Electronics at ACM Multimedia 2009 [16].