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

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 classification. Experiments with Caltech101 and flower image datasets show that the proposed algorithm can effectively improve the accuracy of image classification.

Key words: image classification, Histogram of Oriented Gradient（HOG）, feature extraction, saliency, Support Vector Machines（SVM）