Abstract
We propose an approach to re-ranking images retrieved from existing image search engines based on image quality in aesthetic view. Previous works ranked images based mainly on their relevance to queries. However, it happens that often the top-ranked images cannot satisfy users due to their low visual quality. To present quality images to users, our approach learns a regression model, which combines both conventional and novel image features according to a given quality-image collection. We conducted several experiments on the datasets sampled from INRIA Holiday datasets, Photo.net, DPChallenge, Google Image, and Flickr. The experimental results show the feasibility of the proposed approach in searching aesthetically-pleasing Web-image search results for users.
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Kao, SH., Day, WY., Cheng, PJ. (2010). An Aesthetic-Based Approach to Re-ranking Web Images. In: Cheng, PJ., Kan, MY., Lam, W., Nakov, P. (eds) Information Retrieval Technology. AIRS 2010. Lecture Notes in Computer Science, vol 6458. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17187-1_57
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DOI: https://doi.org/10.1007/978-3-642-17187-1_57
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