Localized Null Space representation for dynamic updating and downdating in image and video databases

Event/object classification and recognition is an extremely challenging problem, particularly when the query or stored data undergo an affine transformation due to camera motion. The complexity of the problem is further compounded when the input or stored data contain only partial information (e.g. due to object occlusion). Most of the existing representation do not allow view invariant representation and dynamic updating and downdating with in a single framework. In this paper, we present a novel robust multi-dimensional Localized Null Space and associated dynamic updating and downdating techniques, thus allowing classification and retrieval in the presence of affine transformations and partial information. We investigate the robustness of Localized Null Space using perturbation analysis. We further determine the optimal segmentation of the data by minimizing a distortion criterion. We demonstrate the effectiveness and robustness of the proposed techniques for motion event classification and retrieval applications by posing different affine transformations of partial queries.