Efficient Object Detection Using Orthogonal NMF Descriptor Hierarchies

Recently descriptors based on Histograms of Oriented Gradients (HOG) and Local Binary Patterns (LBP) have shown excellent results in object detection considering the precision as well as the recall. However, since these descriptors are based on high dimensional representations such approaches suffer from enormous memory and runtime requirements. The goal of this paper is to overcome these problems by introducing hierarchies of orthogonal Non-negative Matrix Factorizations (NMF). In fact, in this way a lower dimensional feature representation can be obtained without loosing the discriminative power of the original features. Moreover, the hierarchical structure allows to represent parts of patches on different scales allowing for a more robust classification. We show the effectiveness of our approach for two publicly available datasets and compare it to existing state-of-the-art methods. In addition, we demonstrate it in context of aerial imagery, where high dimensional images have to be processed requiring efficient methods.