Local Naive Bayes Nearest Neighbor for Image Classification

Technical Report TR-2011-11 University of British Columbia

Local Naive Bayes Nearest Neighbor for Image Classification

Sancho McCann David G. Lowe Department of Computer Science, University of British Columbia 2366 Main Mall, Vancouver, BC, Canada V6T 1Z4 November 30, 2011

Abstract We present Local Naive Bayes Nearest Neighbor, an improvement to the NBNN image classification algorithm that increases classification accuracy and improves its ability to scale to large numbers of object classes. The key observation is that only the classes represented in the local neighborhood of a descriptor contribute significantly and reliably to their posterior probability estimates. Instead of maintaining a separate search structure for each class, we merge all of the reference data together into one search structure, allowing quick identification of a descriptor’s local neighborhood. We show an increase in classification accuracy when we ignore adjustments to the more distant classes and show that the run time grows with the log of the number of classes rather than linearly in the number of classes as did the original. This gives a 100 times speed-up over the original method on the Caltech 256 dataset. We also provide the first head-to-head comparison of NBNN against spatial pyramid methods using a common set of input features. We show that local NBNN outperforms all previous NBNN based methods and the original spatial pyramid model. However, we find that local NBNN, while competitive with, does not beat state-of-the-art spatial pyramid methods that use local soft assignment and max-pooling.

1 Introduction

A widely used approach to object category recognition has been the bag-of-words method [7] combined with the spatial pyramid match kernel [14]. This approach uses visual feature extraction, quantizes features into a limited set of visual words, and performs classification, often with a support vector machine [12, 13]. In contrast to the bag-of-words method, Boiman et al. [3]...