摘要
针对彩色图像信息量大,分割效果自适应性差的问题,对图像语义区域的分割精度进行控制,提取图像的纹理特征值,再通过改进后的概率神经网络模型对测试样本做分类测试,达到提高图像语义提取和分类准确性的目的。实验表明,改进后的概率神经网络对彩色图像语义区域分类的正确性由原先的70%提高到90%,具有较好的分类效果。
For the huge information and poor adaptability of segmentation effect in the colorized images,this article proposes a method to improve the accuracy of extracting and assorting the image semantics.First control the segmentation precision of the image semantics region and extract the image texture characteristics.Then test the samples＇classification through the improved probabilistic neural network model.The experiment shows that the precision of assorting the colorized images semantics region through the improved probabilistic neural network is 90% while it＇s 70% before.The new method gets the better classification effect.
出处
《计算机工程与应用》
CSCD
北大核心
2010年第24期182-184,共3页
Computer Engineering and Applications
基金
国家自然科学基金(No.60702056)~~
关键词
纹理特征
概率神经网络
图像分类
texture characteristics
probabilistic neural network
image classification
