图像检索中一种有效的SVM相关反馈算法

Efficient Relevance Feedback Scheme Based on SVM in Image Retrieval

提出受限随机选择方法.首先对图像进行相似性排序;然后使用一个阈值限定随机选择的范围;最后在该范围内进行划分,在子范围内通过随机选择来扩大训练样本,较好地解决了小样本问题.另外,动态计算多个SVM分类器的权值,融合分类结果,较好地解决了相关反馈过程中用户的不同喜好问题.实验结果表明了该方法的有效性.

An approach called constrained random selection for relevance feedback is proposed in this paper. At first, all the images are sorted by similar measure, and then a threshold is selected to restrict the space of random selection. At last, the restricted space is divided into some sub-spaces, and random selection is applied to these sub-spaces to enlarge the training sets and resolve the small sample problem preferably. In addition, we compute the weights of multiple SVM classifiers dynamically and fuse the single results to resolve the users＇ preference problems in relevance feedback preferably. Experimental results demonstrate the effectiveness of the method.