Relevance-quality ranking based image retrieval

Recent years have witnessed the success of many online social media websites. Social images are usually associated with a set of user-provided descriptors called tags, and thus tag-based search can be easily accomplished by using the descriptors as index terms. However, the existing ranking methods for tag-based image search frequently return results that are irrelevant or noisy with low-quality. It is argued that the relevance and quality are two important measures for a user friendly summarizing the returned images. In this paper, we propose a relevance-quality ranking method considering both image relevance and image quality. First, a relevance-based ranking scheme is utilized to automatically rank images according to their relevance to the query tag, which reckons the relevance scores based on both the visual similarity of images and the semantic consistency of associated tags. Then, quality scores are added to the candidate ranking list to accomplish the relevance-quality based ranking. Experimental results on NUS-WIDE image collection demonstrate the effectiveness of the proposed approach.