Learning Hierarchical Bag of Words Using Naive Bayes Clustering
Venue
Asian Conference on Computer Vision (2012), pp. 382-395
Publication Year
2012
Authors
Siddhartha Chandra, Shailesh Kumar, C. V. Jawahar
BibTeX
Abstract
Image analysis tasks such as classication, clustering, detection, and retrieval are
only as good as the feature representation of the images they use. Much research in
computer vision is focused on finding better or semantically richer image
representations. Bag of visual Words (BoW) is a representation that has emerged as
an eective one for a variety of computer vision tasks. BoW methods traditionally
use low level features. We have devised a strategy to use these low level features
to create \higher level" features by making use of the spatial context in images.
In this paper, we propose a novel hierarchical feature learning framework that uses
a Naive Bayes Clustering algorithm to convert a 2-D symbolic image at one level to
a 2-D symbolic image at the next level with richer features. On two popular
datasets, Pascal VOC 2007 and Caltech 101, we empirically show that classication
accuracy obtained from the hierarchical features computed using our approach is
signicantly higher than the traditional SIFT based BoW representation of images
even though our image representations are more compact.
