Speed up in Computation of HMAX Features for Object Localization

While HMAX features have been proved to have excellent performance in image categorization tasks, the computational cost of recognition is expensive. If we aim to apply the HMAX features to object localization tasks, in which the categorization tasks are repeatedly performed by sliding windows, their processing time increases enormously. In this paper, we propose a method for speed up in computation of object localization based on HMAX features. We found that the HMAX features cause specific redundancies in the sliding window approach. The speed up is achieved by eliminating the redundancies in our method. The results from experiments using the University of Illinois-Urbana-Champaign (UIUC) car dataset and the face detection dataset benchmark (FDDB) indicate that this modification improved processing speeds significantly with insignificant reductions in precision.