摘要
在CBIR研究中,图像低层视觉特征和高层语义特征之间存在的"语义鸿沟"成为语义图像检索的关键问题。为了避免一般映射方法把一幅图像归于一类语义图像的现象,体现自然风景图像中包含的丰富的高层语义信息和多归属类型,提出了对自然风景彩色图像中颜色较单一的目标区域,重复采用最优阈值化进行一次粗分割来提取最大目标区域,在分割区域的基础上,提取图像的局部颜色和形状特征,最后利用改进的模糊神经网络来建立低层视觉特征和高层语义特征之间的映射,实现了图像属性信息的有效传递和高层语义的自动获取。实验结果表明,该图像分割方法对自然彩色图像能够有效地提取目标物体,并对噪声图像具有一定的鲁棒性,而语义图像的部分类别的检索准确率接近90%,查全率也达到了75%,实验结果证明了该方法对自然图像检索的有效性及先进性。
With the development of Content-Based Image Retrieval （CBIR）, the solution of ＂semantic gap＂ which exists between the low-features and the high-level semantic features has become the key problems of the semantic image re- trieval. To avoid the general method maps an image into a class of semantic image, and reflect the natural scenery image contains a wealth of high-level semantic information and multi-homing type, this paper presented a process of repeating use of the optimal threshold for a roughly extraction of the largest target area with the color image. This color target ar- ea is comparatively singleness in the natural scenery images. On the basis of the divided regions, this paper extracted the color and shape features of each region, at last, the fuzzy nerve network was used to map low-features into the high-level semantic features, so it finally realized the image attribute information transfer effectively and obtained the high-level se- mantic automatically. Experimental results show that the method of image segmentation of natural color image can ef- fectively extract the target object. It also has a certain degree of robustness to the noise images. The accurate retrieval rate approaches 90% and the recall rate also achieves 75% in some class image of the nature image database. The experimental result shows the effectiveness and advancement of this method in the natural image retrieval.
出处
《计算机科学》
CSCD
北大核心
2013年第12期122-126,共5页
Computer Science
基金
湖南省自然科学基金(14JJ2074)
"十二五"国家科技支撑计划项目(子项目)
湖南省重点学科建设项目资助
关键词
基于内容的图像检索
语义图像检索
图像分割
最优阈值化
鲁棒性
模糊神经网络
Content-based image retrieval, Semantic image retrieval, Image segmentation, Optimal threshold, Robust- ness, Fuzzy nerve network
