Kolari, P., Finin, F. and Joshi, A. (2006) SVMs for the Blogosphere: Blog Identification and Splog Detection. AAAI Spring Symposium on Computational Approaches to Analysing Weblogs, University of Maryland, Baltimore County.

has been cited by the following article:

• 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.