Latent mixture vocabularies for object categorization and segmentation

Authors:
Diane Larlus;Frédéric Jurie
Affiliations:
INRIA Rhône-Alpes, 655 avenue de l'Europe, Montbonnot, F-38 334 Saint Ismier Cedex, France;INRIA Rhône-Alpes, 655 avenue de l'Europe, Montbonnot, F-38 334 Saint Ismier Cedex, France
Venue:
Image and Vision Computing
Year:
2009

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Abstract

The visual vocabulary is an intermediate level representation which has been proved to be very powerful for addressing object categorization problems. It is generally built by vector quantizing a set of local image descriptors, independently of the object model used for categorizing images. We propose here to embed the visual vocabulary creation within the object model construction, allowing to make it more suited for object class discrimination and therefore for object categorization. We also show that the model can be adapted to perform object level segmentation task, without needing any shape model, making the approach very adapted to high intra-class varying objects.