A strategy of car detection via sparse dictionary

Guo-Qing Jin; Ying-Hui Dong

doi:10.1117/12.896099

8 July 2011 A strategy of car detection via sparse dictionary

Guo-Qing Jin, Ying-Hui Dong

Proceedings Volume 8009, Third International Conference on Digital Image Processing (ICDIP 2011); 80092B (2011) https://doi.org/10.1117/12.896099
Event: 3rd International Conference on Digital Image Processing, 2011, Chengdu, China

Abstract

In recent years there is a growing interest in the study of sparse representation for object detection. These approaches heavily depend on local salient image patches, thus weakening the global contribution to the object identification of other less informative signals.Our generic approach not only employs the informative representation by linear transform, but also keeps all the spatial dependence implied among the objects. As an example,car images can be represented using parts from a vocabulary, along with spatial relations observed among them.Our approach is conducted with the quantitative measurement in developing the car detector at every stage. The theory underneath the optimal solution is the maximal mutual information carried out by the system. Our goal is to keep the maximal mutual information transmitted from stage to stage so that only the least uncertainty about the class identification remains based on the observation of classifier's output.

Citation Download Citation

Guo-Qing Jin and Ying-Hui Dong "A strategy of car detection via sparse dictionary", Proc. SPIE 8009, Third International Conference on Digital Image Processing (ICDIP 2011), 80092B (8 July 2011); https://doi.org/10.1117/12.896099

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