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Leveraging User Comments for Aesthetic Aware Image Search Reranking

Regarding most of my examples, it seems implausible to suggest that a special unifying quality is present, that there is some kind of culmination or that energies have run their proper course. If there is anything that gives these examples a sense of closure, it is that my attention turns away from the moment of experience and moves on to something else. But sometimes the attention is only partially present in such cases; and very often it simply drifts away, rather than being consciously redirected in recognition that a circumscribed moment of experience has come to a close | WWW 2012 - Session Obtaining and Leveraging User Comments April 16-20 2012 Lyon France Leveraging User Comments for Aesthetic Aware Image Search Reranking Jose San Pedro Telefonica Research Barcelona Spain jspw@tid.es Tom Yeh University of Maryland College Park Maryland USA tomyeh@umd.edu Nuria Oliver Telefonica Research Barcelona Spain nuriao@tid.es ABSTRACT 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. Categories and Subject Descriptors H. 3.3 Information Storage and Retrieval Information Search and Retrieval H.5.1 Information Interfaces and Presentation Multimedia Information Systems Keywords opinion mining visual aesthetics modeling image search reranking user comments sentiment analysis I. INTRODUCTION Billions of digital photographs have been shared in photography-centered online communities such as .