Cao Jianfang, Chen Lichao
Department of Computer Science & Technology, Xinzhou Teachers University, Xinzhou 034000, China.
School of Computer Science and Technology, Taiyuan University of Science and Technology, Taiyuan 030024, China.
Comput Intell Neurosci. 2015;2015:971039. doi: 10.1155/2015/971039. Epub 2015 Mar 9.
With the advances in electronic and imaging techniques, the production of digital images has rapidly increased, and the extraction and automated annotation of emotional semantics implied by images have become issues that must be urgently addressed. To better simulate human subjectivity and ambiguity for understanding scene images, the current study proposes an emotional semantic annotation method for scene images based on fuzzy set theory. A fuzzy membership degree was calculated to describe the emotional degree of a scene image and was implemented using the Adaboost algorithm and a back-propagation (BP) neural network. The automated annotation method was trained and tested using scene images from the SUN Database. The annotation results were then compared with those based on artificial annotation. Our method showed an annotation accuracy rate of 91.2% for basic emotional values and 82.4% after extended emotional values were added, which correspond to increases of 5.5% and 8.9%, respectively, compared with the results from using a single BP neural network algorithm. Furthermore, the retrieval accuracy rate based on our method reached approximately 89%. This study attempts to lay a solid foundation for the automated emotional semantic annotation of more types of images and therefore is of practical significance.
随着电子和成像技术的进步，数字图像的产量迅速增加，图像所隐含的情感语义的提取和自动标注已成为亟待解决的问题。为了更好地模拟人类理解场景图像时的主观性和模糊性，本研究提出了一种基于模糊集理论的场景图像情感语义标注方法。通过计算模糊隶属度来描述场景图像的情感程度，并使用Adaboost算法和反向传播（BP）神经网络来实现。使用来自SUN数据库的场景图像对自动标注方法进行训练和测试。然后将标注结果与基于人工标注的结果进行比较。我们的方法在基本情感值上的标注准确率为91.2%，添加扩展情感值后的准确率为82.4%，与使用单一BP神经网络算法的结果相比，分别提高了5.5%和8.9%。此外，基于我们方法的检索准确率达到了约89%。本研究试图为更多类型图像的自动情感语义标注奠定坚实基础，因此具有实际意义。
