Machine Learning
Contextual Priming for Object Detection
International Journal of Computer Vision
Robust Real-Time Face Detection
International Journal of Computer Vision
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
LabelMe: A Database and Web-Based Tool for Image Annotation
International Journal of Computer Vision
Robust Object Detection with Interleaved Categorization and Segmentation
International Journal of Computer Vision
Learning an Alphabet of Shape and Appearance for Multi-Class Object Detection
International Journal of Computer Vision
Hi-index | 0.00 |
In this paper, we present a novel approach for multiclass object detection by combining local appearances and contextual constraints. We first construct a multiclass Hough forest of local patches, which can well deal with multiclass object deformations and local appearance variations, due to randomization and discrimination of the forest. Then, in the object hypothesis space, a new multiclass context model is proposed to capture relative location constraints, disambiguating appearance inputs in multiclass object detection. Finally, multiclass objects are detected with a greedy search algorithm efficiently. Experimental evaluations on two image data sets show that the combination of local appearances and context achieves state-of-the-art performance in multiclass object detection.