为了提高纹理图象分割的边缘准确性和区域一致性以及降低分割错误率, 提出了一种基于小波变换的利

用特征加权来进行纹理分割的方法. 该方法包括特征提取、预分割和后分割 3 个阶段, 其中, 特征提取在金字塔结

构小波变换的基础上进行 预分割利用均值聚类算法来对原始图象进行初步的分割 后分割则根据预分割的结果

对特征进行加权, 然后利用最小距离分类器来实现图象的最后分割. 与传统的方法相比, 该方法在分割错误率、边

缘准确性以及区域一致性等方面均有明显的改善-To imp rove the accuracy of boundary locat ions and region homogeneity as w ell as to reduce the er ro r

rate in texture image segmentat ion, a novel app roach based on w avelet2t ransfo rm and using feature w eigh t ing is

p ropo sed in th is paper . Th is new technique contains th ree consecut ive stages: feature ext ract ion, p re2segmentat ion

and po st2segmentat ion . In the feature ext ract ion stage, texture features are ext racted by using the pyram id2st ruc2

tured w avelet t ransfo rm. The o r iginal image is then segmented init ially using themeans cluster ing algo r ithm in the

p re2segmentat ion stage . A cco rding to the p re2segmentat ion results, the ext racted features are w eigh ted and the

p re2segmented image is fur ther p rocessed w ith a m inimum distance classif ier in the po st2segmentat ion stage to f i2

nally get the segmented image . A ll technical po ints are clear ly descr ibed and p resented in detail . Some segmenta2