NDLI: Leveraging user comments for aesthetic aware image search reranking

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Leveraging user comments for aesthetic aware image search reranking

Content Provider	ACM Digital Library
Author	San Pedro, Jose Oliver, Nuria Yeh, Tom
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.
Starting Page	439
Ending Page	448
Page Count	10
File Format	PDF
ISBN	9781450312295
DOI	10.1145/2187836.2187896
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2012-04-16
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Sentiment analysis User comments Opinion mining Visual aesthetics modeling Image search reranking
Content Type	Text
Resource Type	Article

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