This paper presents a clustering approach

which estimates the specific subspace and the intrinsic dimension of each class. Our approach

adapts the Gaussian mixture model framework to high-dimensional data and estimates

the parameters which best fit the data. We obtain a robust clustering method called High-

Dimensional Data Clustering (HDDC). We apply HDDC to locate objects in natural images

in a probabilistic framework. Experiments on a recently proposed database demonstrate the

effectiveness of our clustering method for category localization.