2,882 research outputs found
Exploring to establish an appropriate model for image aesthetic assessment via CNN-based RSRL: An empirical study
To establish an appropriate model for photo aesthetic assessment, in this
paper, a D-measure which reflects the disentanglement degree of the final layer
FC nodes of CNN is introduced. By combining F-measure with D-measure to obtain
a FD measure, an algorithm of determining the optimal model from the multiple
photo score prediction models generated by CNN-based repetitively self-revised
learning(RSRL) is proposed. Furthermore, the first fixation perspective(FFP)
and the assessment interest region(AIR) of the models are defined and
calculated. The experimental results show that the FD measure is effective for
establishing the appropriate model from the multiple score prediction models
with different CNN structures. Moreover, the FD-determined optimal models with
the comparatively high FD always have the FFP an AIR which are close to the
human's aesthetic perception when enjoying photos
Revisiting Image Aesthetic Assessment via Self-Supervised Feature Learning
Visual aesthetic assessment has been an active research field for decades.
Although latest methods have achieved promising performance on benchmark
datasets, they typically rely on a large number of manual annotations including
both aesthetic labels and related image attributes. In this paper, we revisit
the problem of image aesthetic assessment from the self-supervised feature
learning perspective. Our motivation is that a suitable feature representation
for image aesthetic assessment should be able to distinguish different
expert-designed image manipulations, which have close relationships with
negative aesthetic effects. To this end, we design two novel pretext tasks to
identify the types and parameters of editing operations applied to synthetic
instances. The features from our pretext tasks are then adapted for a one-layer
linear classifier to evaluate the performance in terms of binary aesthetic
classification. We conduct extensive quantitative experiments on three
benchmark datasets and demonstrate that our approach can faithfully extract
aesthetics-aware features and outperform alternative pretext schemes. Moreover,
we achieve comparable results to state-of-the-art supervised methods that use
10 million labels from ImageNet.Comment: AAAI Conference on Artificial Intelligence, 2020, accepte
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