Link-Driven Study to Enhance Text-Based Image Retrieval: Implicit Links Versus Explicit Links

Due to the explosive growth of digital images, new efficient and effective methodologies and tools are needed in the image retrieval field. Compared to the content-based image retrieval approach that suffers from the semantic gap, the text-based image retrieval approach has demonstrated its efficiency in retrieving relevant images for a given query. However, this approach suffers from some limitations. For example, query keywords could not match to the textual content of the document or only some images in a document are relevant to the given query. Therefore, a major challenge of the text-based approach is how to improve the image retrieval accuracy without using the image itself, i.e., by using the surrounding information (context) such as the document structure, the links, etc. To achieve this challenge, some works proposed to explore hyperlinks (explicit links) between documents to re-rank images, while more recent works proposed to automatically build implicit links between images and exploit them in the retrieval process. The aim of this paper is thus to compare the exploration of implicit links versus explicit links, either in image ranking or re-ranking. The Image CLEF 2011 collection on Wikipedia shows that not all top-ranked results are interesting to create and analyze linkages between images. In fact, only the aggregate ranking metric makes notice of the fact that linkages improve image retrieval. We also discover that the retrieval strategy–text-based retrieval with no links, implicit link-based re-ranking, or explicit link-based re-ranking–has a significant impact on the efficiency of the query process.