Paper Info
Title
Local Naive Bayes Nearest Neighbor for image classification
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's training descriptors, 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. Local NBNN gives a 100 times speed-up over the original NBNN 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.
Year
Venue
Keywords
2012
CVPR
local soft assignment,local neighborhood,spatial pyramid method,NBNN image classification algorithm,Local NBNN,Local Naive Bayes Nearest,original NBNN,classification accuracy,previous NBNN,original spatial pyramid model,state-of-the-art spatial pyramid method
DocType
Citations 
PageRank 
Conference
7
0.54
References 
Authors
0
1
Name
Order
Citations
PageRank
D. G. Lowe1157181413.60