Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
International Journal of Computer Vision
Scale & Affine Invariant Interest Point Detectors
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
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
Learning from labeled and unlabeled data on a directed graph
ICML '05 Proceedings of the 22nd international conference on Machine learning
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Weakly Supervised Scale-Invariant Learning of Models for Visual Recognition
International Journal of Computer Vision
LabelMe: A Database and Web-Based Tool for Image Annotation
International Journal of Computer Vision
OPTIMOL: Automatic Online Picture Collection via Incremental Model Learning
International Journal of Computer Vision
Semi-Supervised Learning
Evaluating Color Descriptors for Object and Scene Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pick your neighborhood: improving labels and neighborhood structure for label propagation
DAGM'11 Proceedings of the 33rd international conference on Pattern recognition
Constrained semi-supervised learning using attributes and comparative attributes
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
Augmented attribute representations
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
Semi-Supervised learning on a budget: scaling up to large datasets
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
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Many computer vision methods rely on annotated image sets without taking advantage of the increasing number of unlabeled images available. This paper explores an alternative approach involving unsupervised structure discovery and semi-supervised learning (SSL) in image collections. Focusing on object classes, the first part of the paper contributes with an extensive evaluation of state-of-the-art image representations. Thus, it underlines the decisive influence of the local neighborhood structure and its direct consequences on SSL results and the importance of developing powerful object representations. In a second part, we propose and explore promising directions to improve results by looking at the local topology between images and feature combination strategies.