Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised document classification using sequential information maximization
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
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
Unsupervised Learning of Categories from Sets of Partially Matching Image Features
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Video search reranking via information bottleneck principle
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Unsupervised Object Discovery: A Comparison
International Journal of Computer Vision
Sampling strategies for bag-of-features image classification
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Unsupervised image-set clustering using an information theoretic framework
IEEE Transactions on Image Processing
Information Bottleneck with local consistency
PRICAI'12 Proceedings of the 12th Pacific Rim international conference on Trends in Artificial Intelligence
The multi-feature information bottleneck with application to unsupervised image categorization
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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We present a novel approach to automatically discover object categories from a collection of unlabeled images. This is achieved by the Information Bottleneck method, which finds the optimal partitioning of the image collection by maximally preserving the relevant information with respect to the latent semantic residing in the image contents. In this method, the images are modeled by the Bag-of-Words representation, which naturally transforms each image into a visual document composed of visual words. Then the sIB algorithm is adopted to learn the object patterns by maximizing the semantic correlations between the images and their constructive visual words. Extensive experimental results on 15 benchmark image datasets show that the Information Bottleneck method is a promising technique for discovering the hidden semantic of images, and is superior to the state-of-the-art unsupervised object category discovery methods.