Abstract
Aesthetic perception is a human instinct that is responsive to multimedia stimuli. Giving computers the ability to assess human sensory and perceptual experience of aesthetics is a well-recognized need for the intelligent design industry and multimedia intelligence study. In this work, we constructed a novel database for the aesthetic evaluation of design, using 2,918 images collected from the archives of two major design awards, and we also present a method of aesthetic evaluation that uses machine learning algorithms. Reviewers’ ratings of the design works are set as the ground-truth annotations for the dataset. Furthermore, multiple image features are extracted and fused. The experimental results demonstrate the validity of the proposed approach. Primary screening using aesthetic computing can be an intelligent assistant for various design evaluations and can reduce misjudgment in art and design review due to visual aesthetic fatigue after a long period of viewing. The study of computational aesthetic evaluation can provide positive effect on the efficiency of design review, and it is of great significance to aesthetic recognition exploration and applications development.
Highlights
Computer-aided design evaluation is becoming a well-recognized request in the intelligent design industry
This paper is organized as follows: Section 2 summarizes the previous work on aesthetic evaluation using image processing methods; Section 3 introduces the methods of feature extraction and algorithms, including LibSVM, LibLinear, RBFNetwork, RandomSubSpace, VGG-19 and ResNet-50; Section 4 gives the experimental procedures, including reviewers’ evaluations based on design criteria and the design works evaluation using multi-modal modeling of image features; Section 5 presents the results and discusses the experiments; and Section 6 provides the conclusion of the study and directions for future study
We created an effective method for the aesthetic evaluation of design layouts based on multi-modal image features
Summary
Computer-aided design evaluation is becoming a well-recognized request in the intelligent design industry. The ranking information and reviewers’ ratings are the natural classification annotations of these design images In this experiment, the following image features were extracted as hand-crafted features for aesthetic modeling by LibSVM, LibLinear, RBFNetwork and RandomSubSpace-Randomforest: local binary pattern (LBP), color histogram (HIST), and hue saturation value (HSV). This paper is organized as follows: Section 2 summarizes the previous work on aesthetic evaluation using image processing methods; Section 3 introduces the methods of feature extraction and algorithms, including LibSVM, LibLinear, RBFNetwork, RandomSubSpace, VGG-19 and ResNet-50; Section 4 gives the experimental procedures, including reviewers’ evaluations based on design criteria and the design works evaluation using multi-modal modeling of image features; Section 5 presents the results and discusses the experiments; and Section 6 provides the conclusion of the study and directions for future study
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