TITLE:
Image Classification Based on Histogram Intersection Kernel
AUTHORS:
Hanbin Xi, Tiantian Chang
KEYWORDS:
Classification, Bag of Word, Support Vector Machine, Kernel Function, Visual Keywords
JOURNAL NAME:
Journal of Computer and Communications,
Vol.3 No.11,
November
19,
2015
ABSTRACT:
Histogram Intersection Kernel
Support Vector Machines (SVM) was used for the image classification problem. Specifically,
each image was split into blocks, and each block was represented by the Scale
Invariant Feature Transform (SIFT) descriptors; secondly, k-means cluster
method was applied to separate the SIFT descriptors into groups, each group
represented a visual keywords; thirdly, count the number of the SIFT
descriptors in each image, and histogram of each image should be constructed;
finally, Histogram Intersection Kernel should be built based on these histograms.
In our experimental study, we use Corel-low images to test our method. Compared
with typical RBF kernel SVM, the Histogram Intersection kernel SVM performs
better than RBF kernel SVM.