Making large-scale support vector machine learning practical
Advances in kernel methods
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
ACM SIGGRAPH 2006 Papers
The Design of High-Level Features for Photo Quality Assessment
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
A survey of content-based image retrieval with high-level semantics
Pattern Recognition
A note on Platt's probabilistic outputs for support vector machines
Machine Learning
Learning the consensus on visual quality for next-generation image management
Proceedings of the 15th international conference on Multimedia
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
CAIP '09 Proceedings of the 13th International Conference on Computer Analysis of Images and Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Evaluating Color Descriptors for Object and Scene Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Studying aesthetics in photographic images using a computational approach
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
Realtime aesthetic image retargeting
Computational Aesthetics'10 Proceedings of the Sixth international conference on Computational Aesthetics in Graphics, Visualization and Imaging
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We investigate the use of standard image descriptors and a supervised learning algorithm for estimating the popularity of images. The intended application is in large scale image search engines, where the proposed approach can enhance the user experience by improving the sorting of images in a retrieval result. Classification methods are trained and evaluated on real-world user statistics recorded by a major image search engine. The conclusion is that for many image categories, the combination of supervised learning algorithms and standard image descriptors results in useful popularity predictions.