Improving image search effectiveness by integrating contextual information

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 ImageCLEF¹ benchmark. Obtained results are promising especially for diversity-based ranking.