Name
Abstract | ||
|---|---|---|
Feature redundancy and loss of local feature are central problems for image classification. Feature selection decreases the feature redundancy by choosing a subset of features and eliminating those with low prediction. The local feature representation is able to highlight objects in an image, thus, overcoming the drawbacks of global features. This paper presents a new method, called the local kernel for feature selection, which integrates a local kernel of the segmentation regions into feature selection to provide improved image classification, by means of the region-based image distance integrated into the kernel of the Bayesian classifier. The proposed method is tested on two standard image databases and the classification results are higher than the current feature selection and classification methods. |
Year | Venue | Keywords |
|---|---|---|
2012 | ICPR | global features,image representation,feature subset,bayes methods,local feature representation,region-based image distance,visual databases,multiple local kernel integrated feature selection,feature extraction,image classification,standard image database,bayesian classifier,redundancy,feature redundancy |
Field | DocType | ISSN |
k-nearest neighbors algorithm,Computer vision,Dimensionality reduction,Feature selection,Feature detection (computer vision),Pattern recognition,Feature (computer vision),Computer science,Feature extraction,Artificial intelligence,Kanade–Lucas–Tomasi feature tracker,Linear classifier | Conference | 1051-4651 |
ISBN | Citations | PageRank |
978-1-4673-2216-4 | 0 | 0.34 |
References | Authors | |
8 | 2 | |