利用改进的LZC算法对图像与文字进行分类

Classifying the Image and Text by Using the Improved LZC Algorithm

在传统二值化方法的基础上,将信号序列分为多个区间,进行多尺度二值化粗粒化处理,不增加符号数目,得到更精细,更准确的复杂性.经过实验,分析可知文字与图像随着计算尺度的减小,文字复杂性增大的幅度远小于图像.通过该结论,不同尺度下复杂性的差异可以作为图像与文字分类的特征.因此,我们可以将改进后的LZC算法应用于图像与文字的区分.

Based on the traditional method of binaryzation, this paper divides the signal sequenceinto into several areas anddomulti-scale binaryzation process on the sequence. Under the condition of without increasing the number of symbols, it can get more meticulous and more accuracy complexity. After the experiment, according to our analysis, with the decrease of the calculating scale, the amplitude of the text＇s complexity increasing is much smaller than the image＇s. Acording the conclusion, the difference between the Lempel Ziv complexitys of different scales can be regarded as the feature of image and text. Therefore, it can apply the improved LZC algorithm to distinguish the image and text.