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.
Highlights
With the rapid increase of digital images, it is impossible to label them manually
Chang (BOW) model, we use k-means clustering algorithm to cluster those descriptors into k groups, each of them was regarded as a visual keywords which making up the dictionary, and we can get the histogram of each images, that gives the frequency of each visual keywords contained in the dictionary
Each image is divided into blocks with the equal size B × B on a regular grid, the spatial position can be characterized for each block in an image, and the Scale Invariant Feature Transform (SIFT) descriptors are extracted from each block
Summary
With the rapid increase of digital images, it is impossible to label them manually. How to classify these images quickly and accurately becomes an important research topic currently. How to choose a right kernel is a challenge work in the SVM. Histogram Intersection Kernel SVM was used in the image classification. (2015) Image Classification Based on Histogram Intersection Kernel. T. Chang (BOW) model, we use k-means clustering algorithm to cluster those descriptors into k groups, each of them was regarded as a visual keywords which making up the dictionary, and we can get the histogram of each images, that gives the frequency of each visual keywords contained in the dictionary. Histogram Intersection Kernel should be constructed with these histograms
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