Image Retrieval Using Textual and Texture Properties

Image retrieval is a challenging task that requires efforts from image processing, link structure analysis, and web text retrieval. Since content-based image retrieval is still considered very difficult, most current large scale web image search engines exploit text and link structure to understand the content of the web images. However, local textual information, such as caption, filenames and adjacent text, is not always reliable and informative. And also, there is no commercial web image search engine support RF because of scalability, efficiency and effectiveness. Therefore, global texture information should be taken into account to support RF and a web image retrieval system makes relevance judgement. In this paper, we propose a re-ranking method to improve web image retrieval by reordering the images retrieved from an image search engine using RF. The re-ranking process should be applicable to any image search engines with little effort and experimental results on a database contain three million web images to show RF is effective.