Representing and Recognizing the Visual Appearance of Materials using Three-dimensional Textons
International Journal of Computer Vision
Geometric Hashing: An Overview
IEEE Computational Science & Engineering
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Pictorial Structures for Object Recognition
International Journal of Computer Vision
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discovering Objects and their Localization in Images
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'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
Integrating Representative and Discriminative Models for Object Category Detection
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Efficient Learning of Relational Object Class Models
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Learning Object Categories from Google"s Image Search
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Semantic Image Search and Subset Selection for Classifier Training in Object Recognition
EPIA '09 Proceedings of the 14th Portuguese Conference on Artificial Intelligence: Progress in Artificial Intelligence
From Images to Shape Models for Object Detection
International Journal of Computer Vision
Tutor-based learning of visual categories using different levels of supervision
Computer Vision and Image Understanding
Texture bags: anomaly retrieval in medical images based on local 3d-texture similarity
MCBR-CDS'11 Proceedings of the Second MICCAI international conference on Medical Content-Based Retrieval for Clinical Decision Support
Weakly Supervised Localization and Learning with Generic Knowledge
International Journal of Computer Vision
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Recently, many approaches have been proposed for visual object category detection. They vary greatly in terms of how much supervision is needed. High performance object detection methods tend to be trained in a supervised manner from relatively clean data. In order to deal with a large number of object classes and large amounts of training data, there is a clear desire to use as little supervision as possible. This paper proposes a new approach for unsupervised learning of visual categories based on a scheme to detect reoccurring structure in sets of images. The approach finds the locations as well as the scales of such reoccurring structures in an unsupervised manner. In the experiments those reoccurring structures correspond to object categories which can be used to directly learn object category models. Experimental results show the effectiveness of the new approach and compare the performance to previous fully-supervised methods.