Go to |  Kobus Barnard 's publicationsKobus Barnard 's research pageKobus Barnard 's Home Page

A Statistical Model for General Contextual Object Recognition

Peter Carbonetto, Nando de Freitas, and Kobus Barnard, "A Statistical Model for General Contextual Object Recognition," Eighth European Conference on Computer Vision (to apear)    [ PDF ]

The copyright for the above paper has been transfered to Springer-Verlag. As part of the agreement , Springer-Verlag kindly allows authors to have the text of the papers on their personal web pages and thus the text versions of the paper will remain after publication. The paper will be published in the appropriate Lecture Notes in Computing Science (LNCS)


We consider object recognition as the process of attaching meaningful labels to specific regions of an image, and propose a model that learns spatial relationships between objects. Given a set of images and their associated text (e.g. keywords, captions, descriptions), the objective is to segment an image, in either a crude or sophisticated fashion, then to find the proper associations between words and regions. Previous models are limited by the scope of the representation. In particular, they fail to exploit spatial context in the images and words. We develop a more expressive model that takes this into account. We formulate a spatially consistent probabilistic mapping between continuous image feature vectors and the supplied word tokens. By learning both word-to-region associations and object relations, the proposed model augments scene segmentations due to smoothing implicit in spatial consistency. Context introduces cycles to the undirected graph, so we cannot rely on a straightforward implementation of the EM algorithm for estimating the model parameters and densities of the unknown alignment variables. Instead, we develop an approximate EM algorithm that uses loopy belief propagation in the inference step and iterative scaling on the pseudo-likelihood approximation in the parameter update step. The experiments indicate that our approximate inference and learning algorithm converges to good local solutions. Experiments on a diverse array of images show that spatial context considerably improves the accuracy of object recognition. Most significantly, spatial context combined with a nonlinear discrete object representation allows our models to cope well with over-segmented scenes.

Keywords: Object recognition, correspondence, EM algorithm.

Full text    (pdf)