Abstract: Taxonomy-based architecture data classifying methods and systems are disclosed. A classifier comprises processing nodes arranged in a tree-based architecture. During training mode a classifier module receives descriptions, generates classification predictions, sends the classification predictions to an error calculator to calculate gradients, and receives the gradient while the selector module receives descriptions and annotations associated to the sample piece of data, distributes the descriptions and annotations to child nodes. During testing mode, the classifier module receives descriptions, generates classification predictions and sends the classification predictions to the selector module which receives descriptions and predictions and distributes descriptions to child nodes corresponding to the predictions.

Abstract: Methods and systems of identification of objects in query images are disclosed. Keypoints in the query images are identified corresponding to objects to be identified. Visual words are identified in a dictionary of visual words for the identified keypoints. A set of hits is identified corresponding to reference images comprising the identified keypoints. Reference images corresponding to the identified set of hits are ranked using clustering of matches in a limited pose space. The limited pose space comprises a one-dimensional table corresponding to the rotation between the object to be identified with respect to the reference image. A first subset of M reference images that obtained a rank above a predetermined threshold is then selected. Offline, hybrid and combined offline and hybrid systems for performing the proposed methods are disclosed.