Image Retrieval and Classification Using Local Distance Functions

In this paper we introduce and experiment with a framework for learning local perceptual distance functions for visual recognition.We learn a distance function for each training image as a combination of elementary distances between patch-based visual features.We apply these combined local distance functions to the tasks of image retrieval and classification of novel images.On the Caltech 101 object recognition benchmark, we achieve 60.3% mean recognition across classes using 15 training images per class, which is better than the best published performance by Zhang, et al.