关键词: 图像检索, 基于文本语义相关性的图像检索, 语义相关度, SIFT低层视觉特征, 图像扩展检索

Abstract: An image retrieval algorithm based on text and semantic relevance is proposed to effectively retrieve images, which can satisfy the complex requirement of users. There are two steps in this method：images retrieval based on textual semantic relevance analysis and image extension retrieval based on SIFT features. Firstly, according to the analysis of Natural Language Processing technology, it acquires text keywords and semantic correlation of users’ demands, and applies them to retrieve seed images from the image dataset by semantic relevance analysis. Then image extension retrieval based on SIFT feature are used to conduct extend retrieval in Web image datasets according to the retrieval results of last step. Finally, the paper achieves the query results by combining the retrieval results of these two steps. The experiments on Standard Data Set Corel5K and Web Data Set Deriantart8K prove that this method can achieve more precise image retrieval results by semantic relevance analysis and can enrich the query results by extend retrieval.

Key words: image retrieval, image retrieval based on semantic correlation of text, semantic relevance, low-rise visual features of SIFT, image extension retrieval