A practical approach to feature selection
ML92 Proceedings of the ninth international workshop on Machine learning
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Clustering Algorithms
What Energy Functions Can Be Minimizedvia Graph Cuts?
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Real-Time Face Detection
International Journal of Computer Vision
Learning to Detect Natural Image Boundaries Using Local Brightness, Color, and Texture Cues
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
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
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
Recovering Surface Layout from an Image
International Journal of Computer Vision
Groups of Adjacent Contour Segments for Object Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi-Class Segmentation with Relative Location Prior
International Journal of Computer Vision
Object Recognition by Integrating Multiple Image Segmentations
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
Weakly Supervised Object Localization with Stable Segmentations
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Learning object detection from a small number of examples: the importance of good features
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
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Image segmentation is increasingly used for object recognition. The advantages of segments are numerous: a natural spatial support to compute features, reduction in the number of hypothesis to test, region shape itself can be a useful feature, etc. Since segmentation is brittle, a popular remedy is to integrate results over multiple segmentations of the scene. In previous work, usually all the regions in multiple segmentations are used. However, a typical segmentation algorithm often produces generic regions lacking discriminating features. In this work we explore the idea of finding and using only the regions that are reliable for detection. The main step is to cluster feature vectors extracted from regions and deem as unreliable any clusters that belong to different classes but have a significant overlap. We use a simple nearest neighbor classifier for object class segmentation and show that discarding unreliable regions results in a significant improvement.