PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Category Level Object Segmentation
Diane Larlus and Frederic Jurie
In: International Conference on Computer Vision Theory and Applications, 8-11 March, 2007, Barcelona, Spain.


We propose a new method for learning to segment objects in images. This method is based on a latent variables model used for representing images and objects, inspired by the LDA model. Like the LDA model, our model is capable of automatically discovering which visual information comes from which object. We extend LDA by considering that images are made of multiple overlapping regions, treated as distinct documents, giving more chance to small objects to be discovered. This model is extremely well suited for assigning image patches to objects (even if they are small), and therefore for segmenting objects. We apply this method on objects belonging to categories with high intra-class variations and strong viewpoint changes.

EPrint Type:Conference or Workshop Item (Oral)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
ID Code:3698
Deposited By:Frederic Jurie
Deposited On:14 February 2008