CAJ | 학술논문

为了找到支持向量机（SVM）最佳的分类参数，用以构建适合纹理图像分割的SVM分类器，文中是将基于小生境和交叉选择算子的粒子群算法（ NCSPSO）引入变异算子和族外竞争机制加以改进后与人工鱼群算法（ AFSA）混合，提出了一种改进的NCSPSO-AFSA混合算法优化支持向量机参数，并分别与AFSA算法，粒子群算法（PSO），NCSPSO算法在图像分割准确率、参数寻优时间、图像分割时间等方面进行对比和分析，实验表明文中算法能够更好地获得适用于纹理图像分割的SVM参数，在缩短图像分割时间的同时提高了图像分割准确率，相比较其他算法，文中算法稳健性更好。将此方法应用于电镜及超声纹理图像分割中能较好地提取出目标区域，图像边缘部分的分类也很清晰。
위료조도지지향량궤（SVM）최가적분류삼수，용이구건괄합문리도상분할적SVM분류기，문중시장기우소생경화교차선택산자적입자군산법（ NCSPSO）인입변이산자화족외경쟁궤제가이개진후여인공어군산법（ AFSA）혼합，제출료일충개진적NCSPSO-AFSA혼합산법우화지지향량궤삼수，병분별여AFSA산법，입자군산법（PSO），NCSPSO산법재도상분할준학솔、삼수심우시간、도상분할시간등방면진행대비화분석，실험표명문중산법능구경호지획득괄용우문리도상분할적SVM삼수，재축단도상분할시간적동시제고료도상분할준학솔，상비교기타산법，문중산법은건성경호。장차방법응용우전경급초성문리도상분할중능교호지제취출목표구역，도상변연부분적분류야흔청석。
To find the best parameters of SVM and construct the SVM Classifier which is suitable to be applied to texture image segmentation , the paper improves niche and cross-selection operator PSO where mutation mecha-nism and group competition mechanism are introduced and combined with artificial fish-swarm algorithm ( AF-SA) .The parameter optimization algorithm for support vector machine ( SVM) is proposed .Compared with AF-SA, particle swarm optimization and NCSPSO algorithm in accuracy and time of image segmentation and parame -ter optimization time , it turns out that the proposed algorithm can effectively find the parameters of SVM , cut time and improve accuracy of image segmentation , at the same time , its stability is better than other algorithms . When applied to the electron microscope image and ultrasonic image segmentation , the method it can extract the target area and the image edges are classified quite clearly .