AdaBoost图像到类距离学习的图像分类方法

AdaBoost image-to-class distance learning for image classification

近年来,距离度量学习已经成为图像分类领域的研究热点之一,图像到类距离的度量作为其中的一种方法,取得了不错的分类效果.该方法是一种非参数方法,但由于缺少训练学习,其分类性能很容易受干扰因素的影响,为此提出一种基于AdaBoost算法的图像到类距离学习的图像分类方法.首先将图像到类的距离进行阈值化处理,并使用线性分段函数作为图像到类距离的评价函数,然后将该评价函数作为弱分类器加入到AdaBoost算法中生成一个强分类器.为了选择最优的弱分类器,使用粒子群优化算法确定图像的相似性阈值,再基于权重错误误差最小化原则得到距离评价函数的两个评价值.最后通过实验验证,该方法在Scene⁃15和Caltech⁃101图像数据集上比其他方法有更好的分类效果.

Recently,distance metric learning has become one of the most attractive research areas in image classification.The image⁃to⁃class distance metric is a non⁃parametric method for image classification and achieves a impressive result.However,due to the lack of a training phase,the classification accuracy of it is easily affected by irrelevant factors.In this paper,we propose a novel image⁃to⁃class distance learning method for image classification by using the AdaBoost algorithm.We first deal with image⁃to⁃class distance through the threshold,and a piecewise linear discriminator function is used as the evaluation function of image⁃to⁃class distance.Then,the evaluation function is added to the AdaBoost algorithm as a weak classifier to generate a strong classifier.In order to select the optimal weak classifier,the particle swarm optimization algorithm is used to determine the similarity threshold of the image,and the two evaluation values of the distance evaluation function are obtained based on the principle of weight error minimization.The experimental results on datasets of Scene⁃15 and Caltech⁃101 verified that our proposed method can significantly outperform other methods in image classification.