Academic
Publications
Improving classifiers with unlabeled weakly-related videos

Improving classifiers with unlabeled weakly-related videos,10.1109/CVPR.2011.5995475,Christian Leistner,Martin Godec,Samuel Schulter,Amir Saffari,Manu

Improving classifiers with unlabeled weakly-related videos  
BibTex | RIS | RefWorks Download
Current state-of-the-art object classification systems are trained using large amounts of hand-labeled images. In this paper, we present an approach that shows how to use unlabeled video sequences, comprising weakly-related object categories towards the target class, to learn better classifiers for tracking and detection. The underlying idea is to exploit the space-time consistency of moving objects to learn classifiers that are robust to local transformations. In particular, we use dense optical flow to find moving objects in videos in order to train part-based random forests that are insensitive to natural transformations. Our method, which is called Video Forests, can be used in two settings: first, labeled training data can be regularized to force the trained classifier to generalize better towards small local transformations. Second, as part of a tracking-by-detection approach, it can be used to train a general codebook solely on pair-wise data that can then be applied to tracking of instances of a priori unknown object categories. In the experimental part, we show on benchmark datasets for both tracking and detection that incorporating unlabeled videos into the learning of visual classifiers leads to improved results.
Conference: Computer Vision and Pattern Recognition - CVPR , pp. 2753-2760, 2011
Cumulative Annual
View Publication
The following links allow you to view full publications. These links are maintained by other sources not affiliated with Microsoft Academic Search.