Making large-scale support vector machine learning practical
Advances in kernel methods
Statistical color models with application to skin detection
International Journal of Computer Vision
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
A Bayesian Hierarchical Model for Learning Natural Scene Categories
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
A Visual Vocabulary for Flower Classification
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Efficient Subwindow Search: A Branch and Bound Framework for Object Localization
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Pascal Visual Object Classes (VOC) Challenge
International Journal of Computer Vision
Object Detection with Discriminatively Trained Part-Based Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scalable logo recognition in real-world images
Proceedings of the 1st ACM International Conference on Multimedia Retrieval
Deriving a discriminative color model for a given object class from weakly labeled training data
Proceedings of the 2nd ACM International Conference on Multimedia Retrieval
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We present an approach for automatically devising object annotations in images. Thus, given a set of images which are known to contain a common object, our goal is to find a bounding box for each image which tightly encloses the object. In contrast to regular object detection, we do not assume any previous manual annotations except for binary global image labels. We first use a discriminative color model for initializing our algorithm by very coarse bounding box estimations. We then narrow down these boxes using visual words computed from HOG features. Finally, we apply an iterative algorithm which trains a SVM model based on bag-of-visual-words histograms. During each iteration, the model is used to find better bounding boxes which can be done efficiently by branch and bound. The new bounding boxes are then used to retrain the model. We evaluate our approach for several different classes of publicly available datasets and show that we obtain promising results.