Higher-order dependencies in local appearance models

A novel local appearance modeling method for object detection and recognition in cluttered scenes. The approach is based on the joint distribution of local feature vectors at multiple salient points and their factorization with independent component analysis (ICA). The resulting densities are simple multiplicative distributions modeled through adaptative Gaussian mixture models. This leads to computationally tractable joint probability densities which can model high-order dependencies. Our technique has been initially tested under different natural and cluttered scenes with different degrees of occlusions yielding promising results. In this work, we provide a large statistical test with the MNIST digit database in order to demonstrate the improved performance obtained by explicit modeling of higher-order dependencies.