CATEGORY LEVEL OBJECT SEGMENTATION - Learning to Segment Objects with Latent Aspect Models

Diane Larlus, Frédéric Jurie

2007

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.

Paper Citation

in Harvard Style

Larlus D. and Jurie F. (2007). CATEGORY LEVEL OBJECT SEGMENTATION - Learning to Segment Objects with Latent Aspect Models . In Proceedings of the Second International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, ISBN 978-972-8865-74-0, pages 122-127. DOI: 10.5220/0002050201220127

in Bibtex Style

@conference{visapp07,
author={Diane Larlus and Frédéric Jurie},
title={CATEGORY LEVEL OBJECT SEGMENTATION - Learning to Segment Objects with Latent Aspect Models},
booktitle={Proceedings of the Second International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP,},
year={2007},
pages={122-127},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002050201220127},
isbn={978-972-8865-74-0},
}

in EndNote Style

TY - CONF
JO - Proceedings of the Second International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP,
TI - CATEGORY LEVEL OBJECT SEGMENTATION - Learning to Segment Objects with Latent Aspect Models
SN - 978-972-8865-74-0
AU - Larlus D.
AU - Jurie F.
PY - 2007
SP - 122
EP - 127
DO - 10.5220/0002050201220127