Saliency, Scale and Image Description
International Journal of Computer Vision
The Journal of Machine Learning Research
Learning to Detect Natural Image Boundaries Using Local Brightness, Color, and Texture Cues
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
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
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
Learning Object Categories from Google"s Image Search
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Cosegmentation of Image Pairs by Histogram Matching - Incorporating a Global Constraint into MRFs
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
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
Weakly Supervised Object Localization with Stable Segmentations
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Multiple Component Learning for Object Detection
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Esaliency (Extended Saliency): Meaningful Attention Using Stochastic Image Modeling
IEEE Transactions on Pattern Analysis and Machine Intelligence
Probabilistic latent semantic analysis
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Figure/Ground assignment in natural images
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
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Given a collection of images containing a common object, we seek to learn a model for the object without the use of bounding boxes or segmentation masks. In linguistics, a single document provides no information about location of the topics it contains. On the contrary, an image has a lot to tell us about where foreground and background topics lie. Extensive literature on modelling bottom-up saliency and pop-out aims at predicting eye fixations and allocation of visual attention in a single image, prior to any recognition of content.Most salient image parts are likely to capture image foreground. We propose a novel probabilistic model, shape and figure-ground aware model (sFGmodel) that exploits bottom-up image saliency to compute an informative prior on segment topic assignments. Our model exploits both figure-ground organization in each image separately, as well as feature re-occurrence across the image collection. Since we use image dependent topic prior, during model learning we optimize a conditional likelihood of the image collection given the image bottom-up saliency information. Our discriminative framework can tolerate larger intraclass variability of objects with fewer training data. We iterate between bottom-up figure-ground image organization and model parameter learning by accumulating image statistics from the entire image collection. The model learned influences later image figure-ground labelling. We present results of our approach on diverse datasets showing great improvement over generative probabilistic models that do not exploit image saliency, indicating the suitability of our model for weakly-supervised visual organization.