An LDA Topic Model Adaptation for Context-Based Image Retrieval

In the context-based image retrieval, the textual information surrounding the image plays a central role for ranking returned results. Although this technique outperforms content-based approaches, it may fail when the query keywords does not match the textual content of many documents containing relevant images. In addition, users are usually not experts and provide ambiguous queries that lead to heterogeneous results. To solve these problems, researchers are trying to re-rank primary results using other techniques such as query expansion, concept-based retrieval, etc. In this paper, we propose to use LDA topic model to re-rank results and therefore improve retrieval precision. We apply this model in two levels: global level represented by the whole document containing the image and local level represented by the paragraph containing an image (considered as a specific textual information for the image). Results show a significant improvement over the standard text retrieval approach by re-ranking with the LDA model applied to the local level.