Object recognition using strangeness and transduction
Fayin Li, Jana Košecká, Harry Wechsler · 2006
Object recognition is one of the most essential functionalities of human vision. It is of fundamental importance for machines to be able to learn and recognize objects and to provide a rejection option when the unknown probe has no mates in the set of known objects. This thesis presents novel recognition algorithms using strangeness for generic part-based object recognition and open set recognition, respectively. For part-based object recognition, objects are assumed to be represented in terms of features characterized by descriptors which are invariant to variation of object appearance. This thesis presents a simple and efficient feature selection algorithm to deal with irrelevant and background features and a new non-parametric weak leaner employed in the boosting framework. Specifically a k-Nearest Neighbor strangeness measure is defined to quantify the uncertainty of features with respect to the class labels and used as the criterion to select the discriminative features from the initial feature set to reduce the complexity of learning stage. The boosting learning algorithm is further used to build the final classifier with strangeness as the non-parametric weak learner to characterize the discriminative evidence of each part. This learning approach is able to handle changes in viewpoint, partial occlusion, local deformations, varying illumination and background clutter. We apply and validate the approach on location recognition, part-based face recognition and weakly supervised object category recognition problems. For the latter task, we propose a two-stage learning strategy to distinguish the object from both background and other objects, with the performance and efficiency superior to the state of the art methods. Finally, in the context of face recognition problem, we present the Open Set Transductive Confidence Machine(TCM)-k Nearest Neighbor (kNN) algorithm for open set recognition using strangeness. The algorithm provides a priori availability of a reject option to answer none of the above without modelling the distribution of any object and using the knowledge of unknown classes. It provides a productive solution for the open set recognition problem with a small number of training examples and a large number of classes.