arXiv cs.AI· Nisan Chhetri, Christopher G. Healey, Sina Baghbanijam·· 1 天前AI 评分27
基于新颖性、惊喜度与唤醒度三维度自动评估图像创造力
Estimating Image Creativity
AI 导读
研究者提出一种自动估计图像感知创造力的框架：先用监督机器学习模型预测图像在新颖性、惊喜度和唤醒度三个心理学创造力维度上的强度，再将这些强度组合成创造力评分。在 Amazon Mechanical Turk 上开展的人类受试实验数据显示，该框架给出的估计分数与人类评分在统计上无显著差异。
正文 · 原文
Abstract:What makes an image creative? Although this may seem like a highly subjective decision, research in psychology suggests that creativity may be based on a set of fundamental creativity dimensions such as novelty, surprise, and arousal. A deeper understanding of image creativity can benefit both research and practical applications. Exploring how human viewers use creativity dimensions to evaluate image creativity may allow us to predict a target image's human-rated creativity automatically. We present a novel framework to estimate the perceived creativity of an image by: (1) predicting the strengths of an image's novelty, surprise, and arousal creativity dimensions, and (2) combining the creativity dimension strengths to estimate an image creativity score. Controlled human-subject experiments were conducted on Amazon Mechanical Turk to collect data to train supervised machine learning models to estimate an image's strengths on the three creativity dimensions. A final machine learning model takes the predicted dimension strengths as input to produce an estimated image creativity score. Results showed that our estimated creativity scores are statistically equivalent to human ratings of creativity for the images we tested. The novel contributions of our research are: (1) identification and validation of three psychological creativity dimensions used to estimate image creativity; (2) supervised machine learning models to automatically estimate novelty, surprise, and arousal strengths for an input image; (3) a supervised machine learning model to predict a creativity score from novelty, surprise, and arousal strengths for an input image, and (4) experimental methods to confirm the accuracy, advantages, and limitations of our approach.
| Comments: | 9 pages, 1 figure |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); Machine Learning (cs.LG) |
| Cite as: | arXiv:2611.15044 [cs.CV] |
| (or arXiv:2611.15044v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2611.15044 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Nisan Chhetri [view email]
[v1]
Wed, 11 Nov 2026 05:10:32 UTC (320 KB)
来源：arXiv cs.AI · arxiv.org