An expansion and reranking approach for annotation-based image retrieval from Web

摘要

In this paper, we introduce an expansion and reranking approach for annotation based image retrieval from Web pages. Our suggestion considers an image retrieval system using the surrounding texts nearby the image in a Web page as annotations. However, annotations may include too much and uninformative text such as copyright notice, date, author. In order to choose indexing terms effectively, we propose a term selection approach, which first expands the document using WordNet, and then selects descriptive terms among them. Notably, we applied this term selection methodology to both document and query. This is because applying either of documents or query does not help to increase retrieval performance. On the other hand, term selection process increases the number of terms per documents, and both documents and queries become more exhaustive than original. Consequently, this results high recall with low precision in retrieval. Thus, we also proposed a two-level reranking approach. In order to evaluate our approaches we have participated ImageCLEF2009 WikipediaMM subtask. The results we obtained are superior to any participating approaches and our approach has obtained the best four ranks, in text-only image retrieval. The results also showed that document expansion and effective term selection to annotations plays an important role in text-based image retrieval.