Zero sample image classification method based on relative attribute random forest

The invention provides a zero sample image classification method based on a relative attribute random forest. An attribute ranking score model is built for an image with an unknown class according to the relative relationship between image classes and image attributes, the attribute ranking score models for all images serve as training samples to train a random forest classifier, and finally, according to an attribute ranking score for a tested image and the random forest classifier obtained through training, the label of the tested image is predicted. The method of the invention can realize zero sample image classification and has the advantages of high classification recognition rate, strong model stability and the like. 本发明提出种基于相对属性随机森林的零样本图像分类方法，根据图像类别与图像属性之间的相对关系为未知类别的图像建立属性排序得分模型，将所有图像的属性排序得分模型作为训练样本来训练随机森林分类器，最后根据测试图像的属性排序得分以及训练得到的随机森林分类器对测试图像的标签进行预测。本发明的方法能够实现零样本图像分类，并且具有分类识别率高、模型稳定性强等优点。