An expansion and reranking method for annotation based image retrieval from Web

Current state-of-the-art in image retrieval has two major approaches: content-based image retrieval (CBIR) and annotation based image retrieval (ABIR). Annotation-based image retrieval (ABIR) simply uses text retrieval techniques on annotations generally done by human.In this thesis, we propose a new expansion and reranking method for ABIR from Web images. 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 etc. 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, 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. Experiments have demonstrated that that document expansion and reranking plays an important role in text-based image retrieval and two-level reranking betterments the retrieved results by increasing precision.