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WHAT2PRINT: Learning Image Evaluation
Bohao She and Clark F. Olson In Advances in Visual Computing, Lecture Notes in Computer Science, Vol. 9475 (Eleventh International Symposium on Visual Computing), Part II, pages 597-608, December 2015. The popularity of digital photography has changed the way that images are taken, processed, and stored. This has created a demand for systems that can evaluate the aesthetic quality of images. Applications that auto-assess image aesthetic quality and modify images to raise their aesthetic quality are widely available, but applications that automatically select aesthetic images from a given image collection are limited. The goal of this project is to create a portable application that can recommend user-given images from a given image collection, using criteria learned from user preferences. We train a Support Vector Machine on seven extracted image features. This system achieves a correct prediction rate of 70 % on a public image dataset. The use of additional or improved features should yield increased prediction rates. |
