Charles Bouveyron

Object localization by subspace clustering of local descriptors

This paper presents a probabilistic approach for object localization which combines subspace clustering with the selection of discriminative clusters. Clustering is often a key step in object recognition and is penalized by the high dimensionality of the descriptors. Indeed, local descriptors, such as SIFT, which have shown excellent results in recognition, are high-dimensional and live in different low-dimensional subspaces. We therefore use a subspace clustering method called High-Dimensional Data Clustering (HDDC) which overcomes the curse of dimensionality. Furthermore, in many cases only a few of the clusters are useful to discriminate the object. We, thus, evaluate the discriminative capacity of clusters and use it to compute the probability that a local descriptor belongs to the object. Experimental results demonstrate the effectiveness of our probabilistic approach for object localization and show that subspace clustering gives better results compared to standard clustering methods. Furthermore, our approach outperforms existing results for the Pascal 2005 dataset.

Class-Specific subspace discriminant analysis for high-dimensional data

We propose a new method for discriminant analysis, called High Dimensional Discriminant Analysis (HDDA). Our approach is based on the assumption that high dimensional data live in different subspaces with low dimensionality. We therefore propose a new parameterization of the Gaussian model to classify high-dimensional data. This parameterization takes into account the specific subspace and the intrinsic dimension of each class to limit the number of parameters to estimate. HDDA is applied to recognize object parts in real images and its performance is compared to classical methods.