一种基于SVM和主动学习的图像检索方法

Image retrieval method based on SVM and active learning

为了提高图像检索的速度和准确性,提出了一种基于SVM和主动学习的图像检索方法。该方法分为两个阶段:第一阶段用K-means聚类算法在图像数据库中找出代表性样本,有效地缩小了目标图像的查找范围;第二阶段通过对未标注样本与分类边界之间的距离以及其近邻密度进行综合评价,选出最有价值的关键性样本作为训练样本,使得分类器可以通过较少的反馈次数快速达到较高的准确性。实验表明,该算法可以有效提高图像的检索性能。

In order to improve the speed and accuracy of image retrieval, this paper proposed an image retrieval method based on SVM and active learning. It involved two stages. Fristly,it utilized K-means clustering algorithm to find representative samples from the image database,which may effectively reduce the search range. Secondly,it used a comprehensive evaluation to the unlabeled samples by the distance of the samples with the classification of the border and its neighbor density ,then selected the most valuable samples as training samples, which might the classifier achieve higher accuracy by a small number of feed- back times. Experimental results show that the algorithm can greatly improve the performance of the image retrieval.