计算机工程与应用2019，Vol.55Issue(20)：164-169,6.DOI:10.3778/j.issn.1002-8331.1807-0042
基于多线索特征融合的图像分类方法
Multi-Cue Feature Fusion Based Image Classification
摘要
Abstract
Due to the noise and redundant information in the image, the result of classification is not accurate. This paper proposes a feature fusion classification algorithm that based on multiple clues. Firstly, it gets a significant image by the improved global saliency and rarity metrics. Next, it extracts the Histogram of Oriented Gradient(HOG)features on the original image, including the compressed image and the salient image. And then it merges the extracted feature vectors. At last, it uses Distance Binary Tree Support Vector Machines(DBT-SVM)based on Euclidean distance for image classifica-tion. Experiments with Caltech101 and flower image datasets show that the proposed algorithm can effectively improve the accuracy of image classification.关键词
图像分类/方向梯度直方图/特征提取/显著性/支持向量机Key words
image classification/Histogram of Oriented Gradient(HOG)/feature extraction/saliency/Support Vector Machines(SVM)分类
信息技术与安全科学引用本文复制引用
彭媛,段先华,王万耀,鲁文超..基于多线索特征融合的图像分类方法[J].计算机工程与应用,2019,55(20):164-169,6.基金项目
国家自然科学基金(No.61772244). （No.61772244）
