Recently, there are two possible ways to achieve efficiency of choosing an effective dimension algorithm and using an appropriate relevance feedback technique in image retrieval. Locality Preserving Projections (LPP)is an effective linear dimensionality reduction algorithm, and it preserves the image structure. In order to improve the efficiency of the retrieval accuracy, the article incorporates the users' feedbacks. Using the algorithm of LPP, we map the data points to a subspace. In this subspace, a weighted graph G can be constructed by a candidate data set to consist of k nearest neighbors of the query data points, and query data set. We then compute the geodesic distances between all pairs of vertices of the graph G , and sort them, obtain feedback results. The experimental results show that the algorithm can effectively improve retrieval accuracy, and an optimal retrieval results can be obtained.%目前图像检索通常采用高效的图像降维算法和适当的相关反馈技术来提高检索的效率.局部保形映射(LPP)算法是保留图像本质特征的一种有效的线性降维算法.本文在LPP算法的基础上引入相关反馈技术,进一步提高了检索准确度.利用LPP算法得到降维子空间,在子空间上得出查询数据的k—近邻构成候选数据集,并与查询数据集构建一个权图G,通过弗洛伊德算法求得图G中任意两个数据点之间的测地线距离并排序进而得出反馈结果.实验表明,该算法提高了检索的准确度,并使检索结果得到一定的优化.
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