Accurately clustering Internet-scale Internet users into multiple communities according to their aesthetic styles is a useful technique in image modeling and data mining. In this work, we present a novel partially-supervised model which seeks a sparse representation to capture photo aesthetics 1. It optimally fuzes multi-channel features, i.e., human gaze behavior,quality scores, and semantic tags, each of which could be absent.Afterward, by leveraging the KL-divergence to distinguish the aesthetic distributions between photo sets, a large-scale graph is constructed to describe the aesthetic correlations between users.Finally, a dense sub graph mining algorithm which intrinsically supports outliers (i.e., unique users not belong to any community)is adopted to detect aesthetic communities. Comprehensive experimental results on a million-scale image set crawled from Flickr have demonstrated the superiority of our method. As aby product, the discovered aesthetic communities can enhance photo re targeting and video summarization substantially.
To View the Base Paper Abstract Contents
Now it is Your Time to Shine.
Great careers Start Here.
We Guide you to Every Step
Success! You're Awesome
Thank you for filling out your information!
We’ve sent you an email with your Final Year Project PPT file download link at the email address you provided. Please enjoy, and let us know if there’s anything else we can help you with.
To know more details Call 900 31 31 555
The WISEN Team