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.