摘要
图像中主要对象的提取对于图像语义的抽取以及图像内容的自动标注具有重要作用。提出了一种面向语义的图像主要目标的提取方法。对于分割后的图像,计算不同区域的显著相关色得到核心目标,然后从中提取训练样本集,通过人工神经网络对图像中的块进行分类训练,获得各个区域的主要对象。实验表明,该方法可以较准确地提取出中心区域的对象以及周围区域的对象,并能保留图像的主要信息。
Extraction of image principal objects is of im^x）rtant role both to image semantic extraction and automatic image annotation. A semantic-ori- ented extraction approach of principal objects is proposed. In segmented image,by means of signifieantly correlated color computation in different regions the core objects are obtained and from which the training sample set is extracted. The image patches is trained and classified by artificial neural network, and then the principal objects in regions are extracted. The experiment shows that this approaeh can extract the object in central region and the surround- ing of objeets precisely and keep the information of them.
出处
《电视技术》
北大核心
2013年第5期45-48,共4页
Video Engineering
关键词
颜色相关直方图
显著相关色
神经网络
图像划分
color correlations
significantly correlated color
neural networks
image partition
