Toward an Estimation of User Tagging Credibility for Social Image Retrieval

Existing image retrieval systems exploit textual or/and visual information to return results. The retrieval process is mostly focused on data themselves and disregards the data sources. In Web 2.0 platforms, the quality of annotations provided by different users can vary strongly. To account for this variability, we complement existing methods by introducing user tagging credibility in the retrieval process. Tagging credibility is automatically estimated by leveraging a large set of visual concept classifiers learned with Overfeat, a convolutional neural network (CNN) based feature. A good image retrieval system should return results that are both relevant and diversified and tackle both challenges. We diversify results by using a k-Means algorithm with user based cluster ranking. We increase relevance by favoring images uploaded from users with good credibility estimates. Evaluation is performed on DIV400, a publicly available social image retrieval dataset. Experiments show that our method provides interesting performances compared to existing approaches.