Ranking images based on image aesthetics using a neural network
Abstract: How humans perceive image quality in general (e.g. aesthetics, resolution, lighting) and what constitutes to an image of good quality and aesthetics, is an interesting topic for neural networks to tackle. Lately several different aspects have been investigated in theory to try and build models to accurately predict the quality of an image. The problem is investigated in this thesis by performing an experiment and testing a method that computes the aesthetic value of an image and ranks a number of images according to their aesthetic value. The rankings are then compared to human rankings of the same images to determine if there is a correlation between the two. From the results the null hypothesis (that the correlation is just a coincidence) cannot be rejected but in 92% of the cases there were at least some agreement (same image, same position) between the algorithm and the human ranking. There are multiple similarities between the human rankings and the algorithm rankings which reveals a good potential to further investigate the problem in future.
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