农业工程学报
農業工程學報
농업공정학보
2014年
6期
107-115
,共9页
棉花%算法%纤维%棉花异性纤维%特征选择%费舍尔评分%离散粒子群优化
棉花%算法%纖維%棉花異性纖維%特徵選擇%費捨爾評分%離散粒子群優化
면화%산법%섬유%면화이성섬유%특정선택%비사이평분%리산입자군우화
cotton%algorithms%fibers%cotton foreign fiber%feature selection%fisher score%binary particle swarm optimization
为改进基于机器视觉的棉花异性纤维在线检测效率，提出一种基于费舍尔评分与离散粒子群优化的棉花异性纤维特征选择方法。该方法将费舍尔评分滤波式特征选择方法及基于离散粒子群优化的捆绑式特征选择方法组合在一起，首先利用费舍尔评分方法过滤噪声特征，然后利用离散粒子群算法从已去噪的特征集中选取最优特征子集。提出的方法应用于棉花异性纤维数据集，并与费舍尔评分方法、离散粒子群方法、遗传算法、蚁群算法进行对比，试验结果表明该方法可以更有效地选择出有较少特征数目、较高分类精度的特征子集。从75个棉花异性纤维原始特征中选出18个特征组成的特征集，其分类准确度达到93.5%，检测时间仅为0.8231 s，有效地改进了棉花异性纤维在线检测的精度与效率，从而减少异性纤维对棉纺织品的危害，提高棉纺企业经济效益。
為改進基于機器視覺的棉花異性纖維在線檢測效率，提齣一種基于費捨爾評分與離散粒子群優化的棉花異性纖維特徵選擇方法。該方法將費捨爾評分濾波式特徵選擇方法及基于離散粒子群優化的捆綁式特徵選擇方法組閤在一起，首先利用費捨爾評分方法過濾譟聲特徵，然後利用離散粒子群算法從已去譟的特徵集中選取最優特徵子集。提齣的方法應用于棉花異性纖維數據集，併與費捨爾評分方法、離散粒子群方法、遺傳算法、蟻群算法進行對比，試驗結果錶明該方法可以更有效地選擇齣有較少特徵數目、較高分類精度的特徵子集。從75箇棉花異性纖維原始特徵中選齣18箇特徵組成的特徵集，其分類準確度達到93.5%，檢測時間僅為0.8231 s，有效地改進瞭棉花異性纖維在線檢測的精度與效率，從而減少異性纖維對棉紡織品的危害，提高棉紡企業經濟效益。
위개진기우궤기시각적면화이성섬유재선검측효솔，제출일충기우비사이평분여리산입자군우화적면화이성섬유특정선택방법。해방법장비사이평분려파식특정선택방법급기우리산입자군우화적곤방식특정선택방법조합재일기，수선이용비사이평분방법과려조성특정，연후이용리산입자군산법종이거조적특정집중선취최우특정자집。제출적방법응용우면화이성섬유수거집，병여비사이평분방법、리산입자군방법、유전산법、의군산법진행대비，시험결과표명해방법가이경유효지선택출유교소특정수목、교고분류정도적특정자집。종75개면화이성섬유원시특정중선출18개특정조성적특정집，기분류준학도체도93.5%，검측시간부위0.8231 s，유효지개진료면화이성섬유재선검측적정도여효솔，종이감소이성섬유대면방직품적위해，제고면방기업경제효익。
Foreign fibers in cotton refer to non-cotton fibers and dyed fibers such as hairs, binding ropes, plastic films, candy wrappers, and polypropylene twines. Foreign fibers in cotton even in low content, especially in lint, can seriously affect the quality of the final cotton textile products. Today, online detection systems based on machine vision have been developed for evaluating the quality of the cotton. In such systems, classification of foreign fibers in cotton is the basic and key technology, which is related to the systems’ performance. Finding the optimum feature set with the small size and high accuracy is essential due to it can not only simplify the design of classifier, but also reduce the time of feature extraction. It is a feature selection problem in nature. Feature selection plays an important role in online detection of foreign fibers in cotton. This paper proposed a combined feature selection algorithm for foreign fiber data by combining Fisher Score with BPSO (Binary Particle Swarm Optimization). First, Fisher Score was used to filter noisy features. Then, the BPSO used the classifier accuracy as a fitness function to select the highly discriminating features. The proposed method was tested for classification on foreign fiber dataset. The comparisons of the proposed algorithm with Fisher Score approach and BPSO algorithm showed that the proposed algorithm was able to find the subsets with small size that produced the best classification accuracy in cross-validation. The optimal set with 18 features was selected from 75 features by the proposed algorithm, which classification accuracy reached 93.5%. The time cost of the optimal sets involving three stages corresponding to image segmentation, feature extraction and classification throughout the process of online detection was also tested. The time (0.8231 s) of the optimal set obtained by the proposed algorithm was obviously lower than the original set and the other subset selected by Fscore and BPSO. As a result, the optimal sets obtained by the proposed algorithm was more suitable to online detection and could effectively improve the performance of online detection systems.
