Local Naive Bayes Nearest Neighbor for Image Classification

TL;DR

This paper introduces Local Naive Bayes Nearest Neighbor (NBNN), an improved image classification method that enhances accuracy and scalability by focusing on local neighborhoods, achieving significant speed-ups and outperforming previous NBNN approaches.

Contribution

The paper proposes a novel local NBNN algorithm that merges reference data for faster search and better accuracy, and provides the first direct comparison with spatial pyramid methods.

Findings

Increased classification accuracy over previous NBNN methods.

Run time grows logarithmically with the number of classes, enabling scalability.

Achieves 100x speed-up on Caltech 256 dataset.

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.