Abstract:
In this paper we propose using histogram intersection for mammographic image classification. First, we use the bag-of-words model for image representation, which captures...Show MoreMetadata
Abstract:
In this paper we propose using histogram intersection for mammographic image classification. First, we use the bag-of-words model for image representation, which captures the texture information by collecting local patch statistics. Then, we propose using normalized histogram intersection (HI) as a similarity measure with the K-nearest neighbor (KNN) classifier. Furthermore, by taking advantage of the fact that HI forms a Mercer kernel, we combine HI with support vector machines (SVM), which further improves the classification performance. The proposed methods are evaluated on a galactographic dataset and are compared with several previously used methods. In a thorough evaluation containing about 288 different experimental configurations, the proposed methods demonstrate promising results.
Date of Conference: 14-17 April 2010
Date Added to IEEE Xplore: 21 June 2010
ISBN Information: