针对图像检索中视觉特征和语义信息中的“语义鸿沟”问题，提出一种融合流形学习和相关反馈的人脸图像检索算法．该算法充分考虑相关反馈提供的结合语义信息的正反例样本来发现图像样本之间的鉴别性流形，优化构建低维嵌入空间的特征向量，使得相关图像之间保持近邻关系，通过最大化不相关图像之间的距离，得到一个结合了用户语义理解的低维流形特征空间．实验结果表明: 文中提出的算法有效地融合了图像视觉特征和语义信息，其性能明显优于反馈保局投影、增强联系嵌入等算法，其中前20 个查询结果的检索精度提高了10 个百分点以上．

To narrow down the semantic gap between visual features and semantic information in the retrieval system of face image,a novel retrieval algorithm integrating the manifold learning with the relevant feedback is proposed. In this algorithm,the positive and negative samples containing semantic information,which are provided by the relevance feedback,are taken into consideration to achieve the discriminative manifold embedded in the image space,and a low-dimension manifold space with users' semantic comprehension is obtained by maximizing the gap between the uncorrelated images. Experimental results show that the proposed algorithm effectively integrates the visual features with the semantic information of images,and that it outperforms the algorithms such as the feedback-based locality-preserving projection and the augmented relation embedding,with a retrieval accuracy increasing by 10 points of percentage for the first 20 retrieval results.