Web Image Retrieval using Prior Tags based on WordNet Semantic Information

This research is for early extraction and utilization of semantic information from the tags in tagged Web image retrieval. Generally, users attach a tag to a Web image with little thought of the order, up to over 100 ones. In this paper, we suggest a method of selecting prior tags based on their importance when tagged images are uploaded, and using them in image retrieval. Ideas came from the recognition of the important tags which give a better description of the image as the tags sharing more semantic information with other tags of the same image. This method includes calculation of relation scores between tags based on WordNet and multilevel search of tagged images with the scores. For evaluation, we compared the suggested method and other retrieval methods searching images with simple matching of tags to a given keyword. As the results, we found the superiority of our method in precision and recall rate.