Abstract
Recently, image retrieval approaches shift to context-based reasoning. Context-based approaches proved their efficiency to improve retrieval process. In fact, conventional image search engines are often not able to satisfy the user's intent as they provide noisy or/and redundant results. In addition, when a query is ambiguous, such systems can hardly distinguish different meanings for one query and therefore, they fail to show images with different contexts. A good system should provide, at top-k results, images which are the most relevant and diverse to guarantee user's satisfaction. Our objective is to improve the retrieval process performance by harnessing the contextual information to measure the relevance score and diversity score. The proposed approach implies the relevance-based ranking where a random walk with restart offers a refining step, the diversity-based ranking and the combination. Our approach was evaluated in the context of ImageCLEF(1) benchmark. Obtained results are promising especially for diversity-based ranking.