Modeling High-Order Dependencies in Local Appearance Models
Where Published: Iberian Conference on Pattern Recognition & Image Analysis (IbPRIA'03)
We propose 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 with natural and cluttered scenes with some degree of occlusions yielding promising results. We also propose a method to select a reduced set of learning samples in order to mantain the internal structure of an object to be able to use high-order dependencies reducing the computational load.
