Scene image categorization and video event detection using Naive Bayes Nearest Neighbor

We present a detailed study of Naive Bayes Nearest Neighbor (NBNN) proposed by Boiman et al., with application to scene categorization and video event detection. Our study indicates that using Dense-SIFT along with dimensionality reduction using PCA enables NBNN to obtain state-of-the-art results. We demonstrate this on two tasks: (1) scene image categorization on the UIUC 8 Sports Events Image Dataset (obtaining 84.67%) and the MIT 67 Indoor Scene Image Dataset (obtaining 48.84%); and (2) detecting videos depicting certain events of interest on the challenging MED'11 video dataset with only 15 positive training videos per event. We present an extension referred to as sparse-NBNN that constrains the number of training images that can used to match with a given test image for the image-to-class distance computation. Experiments indicate that this improves upon NBNN for handling of imbalanced training data.