Learning to Detect Objects in Images via a Sparse, Part-Based Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
SIFT-Bag kernel for video event analysis
MM '08 Proceedings of the 16th ACM international conference on Multimedia
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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.