摘要
感性图像检索是一种新型的检索技术,这种检索具有较高的复杂性。一幅图像中能够使人们产生感性认识的可能只有部分区域,准确地找到感兴趣区域能有效地降低复杂度。作为图像基本特征的颜色对人的感觉有重要的影响,颜色的差异和对比使人产生了不同的情感。同时,图像的熵也反应了图像中包含信息量的大小,图像的熵也是引起人们产生感性认识的一个度量。提出了利用图像的颜色和熵提取感兴趣区域进行感性图像检索的方法,通过BP神经网络将感兴趣区域的颜色特征和熵映射到情感特征空间,具有较好的检索效果。
Kansei image retrieval is a new kind of retrieval technology with high complexity. However, it＇ s likely that only some parts of the image would attract people and produce affections. Color imposes a great impact upon the feeling as the basic feature of image, and the difference and comparison of the color would make people produce different kansei. Meanwhile, the entropy of the image also exhibits the information quantity and is a measurement of arousing people＇s kansei. In this paper, we present a method of kansei image retrieval utilizing the color and entropy to extract region of interest（ROI）. Back propagation neural network is employed to map the color and entropy of ROI to affective feature space. Finally, we show some experimental results.
出处
《中国图象图形学报》
CSCD
北大核心
2006年第4期492-497,共6页
Journal of Image and Graphics
基金
国家自然科学基金项目(60372059)
安徽省自然科学基金项目(03042206)
关键词
感性图像检索
感兴趣区域
熵
神经网络
kansei image retrieval, region of interest （ROI） , entropy, neural network
