中文摘要：

图像语义检索的一个有效解决途径是找到图像底层特征与文本语义之间的关联。文中在核方法和图拉普拉斯矩阵的基础上，提出一种相关空间嵌入算法，并利用文本隐性语义索引和图像特征的视觉单词，构造出文本语义空间与图像特征空间这两个异构空间的相关关系，从而找出文本语义与视觉单词间潜在关联，实现图像的语义检索。文中算法把保持数据流形结构的一致性作为一种先验约束，将文本语义空间和图像特征空间中的数据点嵌入到同一个相关空间中。因此，与典型相关分析算法相比，这种相关嵌入映射不仅可揭示不同数据空间之间存在的相关关系，还可在相关空间中保留原始数据分布结构，从而提高算法的可靠性。实验验证文中算法的有效性，为图像语义检索提供一种可行方法。

英文摘要：

An effective approach to semantic-based image retrieval is to find the correlation between low-level visual features and high-level semantics expressed by free text. Inspired by kernel method and graph Laplacian, the correlation space embedding algorithm （ CSEA ） is proposed in this paper. The latent semantic indexing and the visual word are used to construct the correlation between low-level image feature and semantic text feature which are heterogeneous with each other. The underlying cross-modal relationship between the free text and the image is established, and then the semantic-based image retrieval can be realized naturally. The consistency of manifold structure is regarded as a prior constraint in CSEA. By using CSEA, both the low-level image feature and the semantic text feature are embedded into a same intermediate space. Compared with the canonical correlation analysis, the proposed method models the correlation between two different feature spaces and preserves the manifold structure of each＆amp;nbsp;data distribution. Thus, the reliability of the proposed algorithm is improved. The experimental results show the effectiveness and the feasibility of the proposed algorithm in image retrieval.