A Structured Probabilistic Model for Recognition

In this paper we derive a probabilistic model for recognition based on local descriptors and spatial relations between these descriptors. Our model takes into account the variability of local descriptors, their saliency as well as the probability of spatial conngura-tions. It is structured to clearly separate the probability of point-wise correspondences from the spatial coherence of sets of correspondences. For each descriptor of the query image, several correspondences in the image database exist. Each of these point-wise correspondences is weighted by its variability and its saliency. We then search for sets of correspondences which reinforce each other, that is which are spatially coherent. The recognized model is the one which obtains the highest evidence from these sets. To validate our probabilistic model, it is compared to an existing method for image retrieval. The experimental results are given for a database containing more than 1000 images. They clearly show the significant gain obtained by adding the probabilistic model.