A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
Robust Real-Time Face Detection
International Journal of Computer Vision
"GrabCut": interactive foreground extraction using iterated graph cuts
ACM SIGGRAPH 2004 Papers
Combining Top-Down and Bottom-Up Segmentation
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 4 - Volume 04
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
LOCUS: Learning Object Classes with Unsupervised Segmentation
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Shape Guided Object Segmentation
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Using Multiple Segmentations to Discover Objects and their Extent in Image Collections
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
LabelMe: A Database and Web-Based Tool for Image Annotation
International Journal of Computer Vision
POSIT: Part-based object segmentation without intensive training
Pattern Recognition
OPTIMOL: Automatic Online Picture Collection via Incremental Model Learning
International Journal of Computer Vision
Object detection combining recognition and segmentation
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
Object Detection with Discriminatively Trained Part-Based Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
SpatialBoost: adding spatial reasoning to adaboost
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Context models and out-of-context objects
Pattern Recognition Letters
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Numerous approaches to object detection and segmentation have been proposed in recent years. However, in some general situations these methods are prone to fail due to the nature of the object. For instance, classical approaches to object detection and segmentation obtain good results for some specific object classes (i.e., pedestrian detection or sky segmentation). However, these methods have troubles detecting or segmenting object classes with different distinctive characteristics (i.e., cars and horses versus sky and road). In this paper, we propose a general framework to simultaneously perform object detection and segmentation on objects of different nature. Our approach is based on a boosting procedure which automatically decides - according to the object properties - whether it is better to give more weight to the detection or segmentation process to improve both results. For instance, for some objects, the detection may help to better segment, and viceversa. We validate our approach using different object classes from the well-known LabelMe, TUD and Weizmann databases to obtain competitive detection and segmentation results. Furthermore, our experiments show that the proposed approach is able to correctly annotate new images returned by Internet search engines even when the system is trained with few image examples.