With the advance of digital library, the digital content develops with rich information connotation. Traditional information retrieval methods based on external characteristic and text description are unable to sufficiently reveal and express the substance and semantic relation of multimedia information, and unable to fully reveal and describe the representative characteristics of information. Because of the abundant connotation of image content and the people's abstract subjectivity in studying image content, the visual feature of the image is difficult to be described by key words. Therefore, this method not always can meet people's needs, and the study of digital library image retrieval technique based on content is important to both academic research and application. At present, image retrieval methods are mainly based on the text and content, etc. But these existing algorithms have shortages, such as large errors and slow speeds. Motivated by the above fact, we in this paper propose a new approach based on relevance vector machine (RVM). The proposed approach first extracts the patch-level scale invariant image feature (SIFT), and then constructs the global features for images. The image feature is then delivered into RVM for retrieval. We evaluate the proposed approach on Corel dataset. The experimental result shows that the proposed method in this text has high accuracy when retrieves images. [ABSTRACT FROM AUTHOR]
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