Integrating bilingual search results for automatic junk image filtering

Abstract

Keyword-based image search engines are now very popular for accessing large amounts of Web images on the Internet. Most existing keyword-based image search engines may return large amounts of junk images (which are irrelevant to the given query word), because the text terms that are loosely associated with the Web images are also used for image indexing. The objective of the proposed work is to effectively filter out the junk images from image search results. Therefore, bilingual image search results for the same keyword-based query are integrated to identify the clusters of the junk images and the clusters of the relevant images. Within relevant image clusters, the results are further refined by removing the duplications under a coarse-to-fine structure. Experiments for a large number of bilingual keyword-based queries (5,000 query words) are simultaneously performed on two keyword-based image search engines (Google Images in English and Baidu Images in Chinese), and our experimental results have shown that integrating bilingual image search results can filter out the junk images effectively.