Traditional Content Based Multimedia Retrieval (CBMR) systems measure the relevance of visual samples using a binary scale (Relevant/Non Relevant). However, a picture can be relevant to a semantic category with different degrees, depending on the way such concept
is represented in the image. In this paper, we build a CBMR framework that supports graded relevance judgments. In order to quickly build graded ground truths, we propose a measure to reassess binary-labeled databases without involving manual effort: we automatically assign a reliable relevance degree (Non, Weakly, Average, Very Relevant) to each sample, based on its position with respect to the hyperplane drawn by support vector machines in the feature space. We test the effectiveness of our system on two large-scale databases, and we show that our approach outperforms the traditional binary relevance-based frameworks in both scene recognition and video retrieval.
