Recently, the Gaussianized vector representation has been shown effective in several applications related to interactive multimedia, such as facial age estimation, image scene categorization and video event recognition. However, all these tasks are classification and regression problems based on the whole images. It is not yet explored how this representation can be efficiently applied in the object localization problem, which reveals the locations and sizes of the objects. In this paper, we present an efficient object localization approach for the Gaussianized vector representation, following a branch-and-bound search scheme introduced by Lampert et al. In particular, we design a quality bound for rectangle 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 representation for the whole image. A localization experiment on a multi-scale car dataset shows that the proposed object localization approach based on the Gaussianized vector representation outperforms previous work using the histogram-of-keywords representation.