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Object recognition is an active research topic in the computer vision community. Recently a novel Image-to-Class (I2C) distance has been proposed to handle this problem, which classifies images using a simple Naive-Bayes based nearest-neighbor (NBNN) classifier but provides surprisingly excellent performance. This new distance provides a novel direction that avoids feature quantization and shows better generalization capability than the traditional Image-to-Image (I2I) distance. However, the computation cost of calculating this distance is too expensive since its performance relies heavily on searching the nearest neighbor (NN) from a large number of training features, and the label information of the training data is not fully used, which limits its recognition performance. In this thesis, we aim to improve both the recognition performance and efficiency of this I2C distance as well as to extend its application field. First of all, we add a training phase to this distance for improving its recognition performance by learning a weighted I2C distance. A large margin optimization framework is proposed to learn the I2C distance function, which is modeled as a weighted combination of the distance from every local feature in an image to its NN in a candidate class. We learn these weights associated with local features in the training set by constraining the optimization such that the I2C distance from image to its belonging class should be less than that to any other class. To reduce the computation cost, we also propose two methods based on spatial division and hubness score to accelerate the NN search, which is able to largely reduce the online testing time while still preserving or even achieving a better classification accuracy. Secondly, we propose a distance metric learning method to further improve the performance of I2C distance by learning Per-Class Mahalanobis metrics. This Mahalanobis I2C distance is adaptive to different classes by combining with the learned metric for each class. These multiple Per-Class metrics are learned simultaneously by forming a convex

Distance learning between image and class for object recognition