一种改进的SVM相关反馈图像检索方法*

提出了一种改进的支持向量机SVM( Support Vector Machine) 的相关反馈图像检索方法。在这种方法的交互过程中, SVM 分类器不仅对本次反馈过程中用户所提交的标记的正例和反例样本进行学习, 还对历次反馈过程中的正例和反例样本进行学习, 并根据训练后的分类器进行检索。实验结果表明, 该方法在样本集非常小的情况下, 仍可以检索出较多的相关图像, 在有限训练样本情况下具有良好的推广能力。

An Improved Image Retrieval Approach Based on Support Vector Machine

Developed a improve image retrieval approach based on SVM. During the interactive procedure, the positive and negative sample images respect to the image marked by users both in current circulation and in the historical circulation are learned for constructing a SVM classifier, with which we classify the database images again. Experiments demonstrated that more relevant can be found efficiently by the interactive method with a number of procedure even when the number of the samples in each circulation is a little. In addition, it has the generalization ability of limited training samples.