Synthesizing photo products such as photo strips and slideshows using a database of images is a time-consuming and tedious process that requires significant manual work. To overcome this limitation, we developed a method that automatically synthesizes photo sequences based on several design parameters. Our method considers the valence and arousal ratings of images in conjunction with parameters related to both the visual consistency of the synthesized photo sequence and the progression of valence and arousal throughout the photo sequence. Our method encodes valence, arousal, and visual consistency parameters as cost terms into a total cost function while applying a Markov chain Monte Carlo optimization techniques called simulated annealing to synthesize the photo sequence based on user-defined target objectives in a few seconds. As our method was developed for the synthesis of photo sequences using the valence-arousal emotional model, a user study was conducted to evaluate the efficacy of the synthesized photo sequences in triggering valence-arousal ratings as expected. Our results indicate that the proposed method synthesizes photo sequences in which valence and arousal dimensions are perceived as expected by participants; however, valence may be more appropriately perceived than arousal.

中文翻译：

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不同情绪的照片序列：使用价唤醒注释数据集进行优化

使用图像数据库合成照片产品（例如照片条和幻灯片）是一个耗时且乏味的过程，需要大量的手动工作。为了克服这一限制，我们开发了一种基于多个设计参数自动合成照片序列的方法。我们的方法考虑了图像的效价和唤醒等级以及与合成照片序列的视觉一致性以及整个照片序列的效价和唤醒进展相关的参数。我们的方法将效价、唤醒和视觉一致性参数作为成本项编码到总成本函数中，同时应用称为模拟退火的马尔可夫链蒙特卡罗优化技术在几秒钟内根据用户定义的目标目标合成照片序列。由于我们的方法是为使用效价唤醒情绪模型合成照片序列而开发的，因此进行了一项用户研究，以评估合成照片序列在按预期触发效价唤醒评级方面的功效。我们的结果表明，所提出的方法合成的照片序列中，效价和唤醒维度被参与者所感知。然而，效价可能比唤醒更恰当地被感知。