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Category level object segmentation - learning to segment objects with latent aspect models

Diane Larlus 1 Frédéric Jurie 1
1 LEAR - Learning and recognition in vision
Grenoble INP [2007-2019] - Institut polytechnique de Grenoble - Grenoble Institute of Technology [2007-2019], LJK [2007-2015] - Laboratoire Jean Kuntzmann [2007-2015], Inria Grenoble - Rhône-Alpes
Abstract : 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.
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Submitted on : Monday, December 20, 2010 - 10:28:44 AM
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  • HAL Id : inria-00548681, version 1



Diane Larlus, Frédéric Jurie. Category level object segmentation - learning to segment objects with latent aspect models. VISAPP - 2nd International Conference on Computer Vision Theory and Applications, Mar 2007, Barcelona, Spain. pp.122-127. ⟨inria-00548681⟩



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