Natural color image enhancement and evaluation algorithm based on human visual system
Computer Vision and Image Understanding
Ranking and classifying attractiveness of photos in folksonomies
Proceedings of the 18th international conference on World wide web
Prediction of favourite photos using social, visual, and textual signals
Proceedings of the international conference on Multimedia
The role of attractiveness in web image search
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Leveraging user comments for aesthetic aware image search reranking
Proceedings of the 21st international conference on World Wide Web
High level describable attributes for predicting aesthetics and interestingness
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
ICME '12 Proceedings of the 2012 IEEE International Conference on Multimedia and Expo
Where is the beauty?: retrieving appealing VideoScenes by learning Flickr-based graded judgments
Proceedings of the 20th ACM international conference on Multimedia
MoodScope: building a mood sensor from smartphone usage patterns
Proceeding of the 11th annual international conference on Mobile systems, applications, and services
Hi-index | 0.00 |
We analyze behavior patterns and photographic habits of the Nokia Mobile Data Challenge (NMDC) participants using GPS and time-stamp data. We show that these patterns and habits can be used to estimate image appeal ratings of geotagged Flickr images. In order to do this, we summarize the behavior patterns of the individual NMDC participants into rare and repeating events using GPS coordinates and time stamps. We then retrieve, based on both the time and location information from these events, geotagged images and their "view" and "favorite" counts from Flickr. The appeal of an image is calculated as the ratio of favorite count to view count. We analyze how rare and repeating events are related to the appeal of the downloaded Flickr images and find that image appeal ratings are higher for events when the NMDC participants also took pictures and also higher for rare events. We thus design new event-based features to rate and rank the geotagged Flickr images. We measure the ranking performance of our algorithm by using the Flickr appeal ratings as ground truth. We show that our event-based features outperform visual-only features, which were previously used in image appeal ratings, and obtain a Spearman's correlation coefficient of 0.47.