A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Segmentation of Color-Texture Regions in Images and Video
IEEE Transactions on Pattern Analysis and Machine Intelligence
Saliency, Scale and Image Description
International Journal of Computer Vision
Blobworld: Image Segmentation Using Expectation-Maximization and Its Application to Image Querying
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple-Instance Learning for Natural Scene Classification
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Multiple instance learning with generalized support vector machines
Eighteenth national conference on Artificial intelligence
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Textons, Contours and Regions: Cue Integration in Image Segmentation
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Image Categorization by Learning and Reasoning with Regions
The Journal of Machine Learning Research
Object Class Recognition by Boosting a Part-Based Model
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
A Sparse Support Vector Machine Approach to Region-Based Image Categorization
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Spectral Segmentation with Multiscale Graph Decomposition
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Generic Object Recognition with Boosting
IEEE Transactions on Pattern Analysis and Machine Intelligence
Extracting Subimages of an Unknown Category from a Set of Images
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Model Order Selection and Cue Combination for Image Segmentation
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
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
Using Dependent Regions for Object Categorization in a Generative Framework
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
MILES: Multiple-Instance Learning via Embedded Instance Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Weakly Supervised Scale-Invariant Learning of Models for Visual Recognition
International Journal of Computer Vision
Exploiting low-level image segmentation for object recognition
DAGM'06 Proceedings of the 28th conference on Pattern Recognition
Weakly supervised learning of part-based spatial models for visual object recognition
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Automatic attribute discovery and characterization from noisy web data
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Localizing objects while learning their appearance
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
ClassCut for unsupervised class segmentation
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Figure-ground image segmentation helps weakly-supervised learning of objects
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
Weakly supervised landmark labeling in searched data
ICIMCS '10 Proceedings of the Second International Conference on Internet Multimedia Computing and Service
Object class segmentation using reliable regions
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part II
Multiple instance learning with missing object tags
Proceedings of the Third International Conference on Internet Multimedia Computing and Service
Exploiting context aware category discovery for image labeling
Proceedings of the Third International Conference on Internet Multimedia Computing and Service
Accurate Object Recognition with Shape Masks
International Journal of Computer Vision
Categorization of multiple objects in a scene without semantic segmentation
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part I
Weakly Supervised Localization and Learning with Generic Knowledge
International Journal of Computer Vision
TriCoS: a tri-level class-discriminative co-segmentation method for image classification
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
Instance-Level landmark labeling via multi-layer superpixels
PCM'12 Proceedings of the 13th Pacific-Rim conference on Advances in Multimedia Information Processing
Knowledge leverage from contours to bounding boxes: a concise approach to annotation
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
Learning discriminative localization from weakly labeled data
Pattern Recognition
Hi-index | 0.00 |
Multiple Instance Learning (MIL) provides a framework for training a discriminative classifier from data with ambiguous labels. This framework is well suited for the task of learning object classifiers from weakly labeled image data, where only the presence of an object in an image is known, but not its location. Some recent work has explored the application of MIL algorithms to the tasks of image categorization and natural scene classification. In this paper we extend these ideas in a framework that uses MIL to recognize and localizeobjects in images. To achieve this we employ state of the art image descriptors and multiple stable segmentations. These components, combined with a powerful MIL algorithm, form our object recognition system called MILSS. We show highly competitive object categorization results on the Caltech dataset. To evaluate the performance of our algorithm further, we introduce the challenging Landmarks-18 dataset, a collection of photographs of famous landmarks from around the world. The results on this new dataset show the great potential of our proposed algorithm.