Detecting Pedestrians Using Patterns of Motion and Appearance
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
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
Orthogonal nonnegative matrix t-factorizations for clustering
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Fast Human Detection Using a Cascade of Histograms of Oriented Gradients
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Robust Object Detection with Interleaved Categorization and Segmentation
International Journal of Computer Vision
Orthogonal Nonnegative Matrix Factorization: Multiplicative Updates on Stiefel Manifolds
IDEAL '08 Proceedings of the 9th International Conference on Intelligent Data Engineering and Automated Learning
A local basis representation for estimating human pose from cluttered images
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part I
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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.