Work

Image Classification Based on Histogram Intersection Kernel

Year: 2015

Type: article

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.

Source: Journal of Computer and Communications

DOI: 10.4236/jcc.2015.311025

Authors: Hanbin Xi, Tiantian Chang

Institution: Xi’an University of Posts and Telecommunications

Language: English

Topic: Image Retrieval and Classification Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

Funders: National Natural Science Foundation of China, Xi'an University of Posts and Telecommunications, Education Department of Shaanxi Province

Award: G8469022051