document: Leveraging user comments for aesthetic aware image search reranking The increasing number of images available online has created a growing need for efficient ways to search for relevant content . Text-based query search is the most common approach to retrieve images from the Web . In this approach , the similarity between the input query and the metadata of images is used to find relevant information . However , as the amount of available images grows , the number of relevant images also increases , all of them sharing very similar metadata but differing in other visual characteristics . This paper studies the influence of visual aesthetic quality in search results as a complementary attribute to relevance . By considering aesthetics , a new ranking parameter is introduced aimed at improving the quality at the top ranks when large amounts of relevant results exist . Two strategies for aesthetic rating inference are proposed : one based on visual content , another based on the analysis of user comments to detect opinions about the quality of images . The results of a user study with $ 58 $ participants show that the comment-based aesthetic predictor outperforms the visual content-based strategy , and reveals that aesthetic-aware rankings are preferred by users searching for photographs on the Web .
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