基于改进差分进化算法的鳞翅目昆虫图像识别方法

doi:10.11707/j.1001-7488.20200308

摘要/Abstract

摘要：

目的: 应用计算机图像处理技术提取昆虫图像特征，提出一种新的特征选择技术筛选昆虫识别相关的特征，以准确、快速地识别出鳞翅目昆虫种类。方法: 基于开源的利兹蝴蝶数据集和拍摄的以森林鳞翅目昆虫为主的数据集，采用改进的纹理特征提取算法（DRLBP）提取鳞翅目昆虫图像纹理特征，应用汉明距离计算的粒子间距离度量种群多样性，提出进化过程中自动调整多样性的方法，给出二进制自适应差分进化算法（BADE）。利用BADE算法筛选合适的较小维数的纹理特征子集，并用基于概率协同表示的分类器（PROCRC）进行图像分类。结果: PROCRC分类器在所有数据集上均展现出良好分类效果，平均识别率分别为81.73%和88.18%。经特征选择后的昆虫的分类精度显著提升，最高提升率达13.49%。BADE的性能高于其他特征选择算法，且经BADE算法特征选择后纹理数据集的维数和分类所需时间均显著下降，其降维率接近50%，时间减少率最高达50%。结论: BADE算法可有效进行特征选择，提高识别精度，节约模型的识别时间，利用群体智能优化算法对鳞翅目昆虫图像进行特征选择的方法具有可行性，DRLBP和BADE算法相结合的鳞翅目昆虫识别方法在农林昆虫的快速、准确识别中具有广阔应用前景。

关键词: 纹理特征, 二进制自适应差分进化, 特征选择, 分类器

Abstract:

Objective: In this study, based on computer image processing technology to extract insect image features, a new feature selection technique was proposed to screen features related insect identification. The technique is developed for accurately and quickly identifying the species of Lepidopteran insects. Method: In order to improve the universality of the model, the open-source Leeds butterfly dataset and the dataset of the forest Lepidopteran insects were used in this study. The improved texture feature extraction algorithm (DRLBP) was used to extract the texture features of Lepidopteran insect images to obtain high recognition accuracy. The hamming distance was used to calculate the distance between particles and measure the population diversity. A method of automatically adjusting the diversity in the evolutionary process was proposed. A binary adaptive differential evolution (BADE) algorithm was presented. The BADE algorithm were used to select the appropriate subset of texture features with small dimensions, and probabilistic collaborative representation based classifier (PROCRC) was used to classify images. Result: PROCRC classifier showed good classification result on all datasets. Its average recognition rate was 81.73% and 88.18% respectively. The classification accuracy of insects was significantly improved by feature selection. Its highest rate of improvement was 13.49%. The performance of BADE algorithm were better than other feature selection algorithms. The dimension and classification time of texture dataset selected by the BADE algorithm was significantly reduced. The dimensionality reduction rate was close to 50%, and the time reduction rate was up to 50%. Conclusion: The BADE algorithm proposed in this study can effectively carry out feature selection, which improves the recognition accuracy and saves the recognition time of the model. It is indicated that the method of feature selection for Lepidopteran insect images using swarm intelligence optimization algorithm is feasible. In summary, the method of recognition of Lepidopteran Insects combined with DRLBP and BADE algorithm is proposed in this study, which has important application prospects in rapid and accurate identification of agricultural and forestry insects.

Key words: texture feature, binary adaptive differential evolution, feature selection, classifier

中图分类号:

S763.42

林达坤,黄世国,张飞萍,梁光红,吴松青,胡霞,王荣. 基于改进差分进化算法的鳞翅目昆虫图像识别方法[J]. 林业科学, 2020, 56(3): 73-81.

Dakun Lin,Shiguo Huang,Feiping Zhang,Guanghong Liang,Songqing Wu,xia Hu,Rong Wang. Method of Image Recognition for Lepidopteran Insects Based on Improved Differential Evolution Algorithm[J]. Scientia Silvae Sinicae, 2020, 56(3): 73-81.

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数据集 Datasets	分类器 Classifiers	不同运行次数的识别率 Recognition rates of different running times(%)					均值 Average	显著性水平 Significance level
数据集 Datasets	分类器 Classifiers	1	2	3	4	5	均值 Average	显著性水平 Significance level
利兹Leeds	PROCRC	82.08	81.13	81.61	82.21	81.61	81.73	＜0.001
利兹Leeds	KNN	38.59	40.38	39.68	39.30	40.13	39.61	＜0.001
本课题组 Taken in this study	PROCRC	88.25	88.30	88.02	88.70	87.62	88.18	＜0.001
本课题组 Taken in this study	KNN	84.05	83.99	83.76	84.56	83.02	83.87	＜0.001

数据集 Datasets	种类 Species	样本数量 Number of samples	PROCRC识别率 Recognition rate of PROCRC(%)	KNN识别率 Recognition rate of KNN(%)	显著性水平 Significance level
利兹Leeds	黑脉金斑蝶Danaus plexippus	82	95.07	37.65	＜0.001
	黄条袖蝶Heliconius charitonius	93	97.89	80.53	0.01
	红带袖蝶Heliconius erato	61	64.23	0.00	＜0.001
	鹿眼蛱蝶Junonia coenia	90	70.00	45.56	0.01
	红灰蝶Lycaena phlaeas	88	79.48	47.52	＜0.001
	丧服蛱蝶Nymphalis antiopa	100	82.00	47.00	＜0.001
	美洲大芷凤蝶Papilio cresphontes	89	77.39	41.44	＜0.001
	白粉蝶Pieris rapae	55	98.18	72.73	＜0.001
	优红蛱蝶Vanessa atalanta	90	64.44	10.00	＜0.001
	小红蛱蝶Vanessa cardui	84	91.69	13.09	＜0.001

本课题组 Taken in this study	檗黄粉蝶Eurema blanda	7	50.00	20.00	0.35
	茶蚕蛾Andraca bipunctata	238	95.39	85.28	＜0.001
	东方粉蝶Pieris canidia	12	23.33	0.00	0.08
	斐豹蛱蝶Argynnis hyperbius	5	20.00	0.00	0.37
	黄星尺蛾Arichanna melanaria fraternal	167	91.59	91.60	1.00
	灰白蚕蛾Ocinara varians	200	99.50	100.00	0.37
	蕾鹿蛾Amata germana	16	88.33	51.67	0.09
	柳杉毛虫Dendrolimus houi	372	96.23	91.12	0.01
	绿尾大蚕蛾Actias selene ningpoana	9	100.00	80.00	0.18
	马尾松毛虫Dendrolimus punctatus	359	82.46	80.50	0.62
	榕透翅毒蛾Perina nuda	349	83.97	80.51	0.39
	长喙天蛾Macroglossum corythus luteata	27	14.67	3.33	0.05

数据集 Datasets	分类器 Classifier	不同特征选择方法的识别率 Recognition rate of different feature selection approaches(%)
数据集 Datasets	分类器 Classifier	基于频繁出现模式 Based on the most frequently occurring patterns	二进制差分进化 Binary differential evolution	二进制自适应差分进化 Binary adaptive differential evolution
利兹Leeds	PROCRC	65.92	85.02	87.54
利兹Leeds	KNN	52.25	49.54	53.10
本课题组 Taken in this study	PROCRC	84.55	88.86	89.84
本课题组 Taken in this study	KNN	84.11	86.16	86.96

数据集 Datasets	种类 Species	样本数量 Number of samples	识别率 Recognition rate (%)		显著性水平 Significance level
数据集 Datasets	种类 Species	样本数量 Number of samples	PROCRC	KNN	显著性水平 Significance level
利兹Leeds	黑脉金斑蝶Danaus plexippus	82	97.5	67.06	＜0.001
	黄条袖蝶Heliconius charitonius	93	94.62	87.13	0.11
	红带袖蝶Heliconius erato	61	72.18	9.74	＜0.001
	鹿眼蛱蝶Junonia coenia	90	80	56.67	＜0.001
	红灰蝶Lycaena phlaeas	88	84.18	64.9	0.02
	丧服蛱蝶Nymphalis antiopa	100	85	51	0.02
	美洲大芷凤蝶Papilio cresphontes	89	85.42	45.95	0.01
	白粉蝶Pieris rapae	55	98.18	70.91	0.01
	优红蛱蝶Vanessa atalanta	90	58.89	22.22	＜0.001
	小红蛱蝶Vanessa cardui	84	87.87	22.57	＜0.001

本课题组 Taken in this study	檗黄粉蝶Eurema blanda	7	50	40	0.40
	茶蚕蛾Andraca bipunctata	238	92.87	84.45	0.09
	东方粉蝶Pieris canidia	12	10	0	0.18
	斐豹蛱蝶Argynnis hyperbius	5	0	0	—
	黄星尺蛾Arichanna melanaria fraternal	167	93.44	92.21	0.87
	灰白蚕蛾Ocinara varians	200	99	99.5	1.00
	蕾鹿蛾Amata germana	16	86.67	68.33	0.13
	柳杉毛虫Dendrolimus houi	372	96.5	92.19	＜0.001
	绿尾大蚕蛾Actias selene ningpoana	9	100	100	0.37
	马尾松毛虫Dendrolimus punctatus	359	82.74	80.23	0.33
	榕透翅毒蛾Perina nuda	349	83.94	82.24	0.94
	长喙天蛾Macroglossum corythus luteata	27	3.33	14.67	0.92

数据集 Datasets	分类器 Classifier	不同特征选择的F1值F1-score of different feature selection methods(%)
数据集 Datasets	分类器 Classifier	基于频繁出现模式 Based on the most frequently occurring patterns	二进制差分进化 Binary differential evolution	二进制自适应差分进化 Binary adaptive differential evolution
利兹Leeds	PROCRC	61.18	85.33	87.29
利兹Leeds	KNN	50.86	48.69	53.92
本课题组 Taken in this study	PROCRC	83.61	88.66	89.03
本课题组 Taken in this study	KNN	83.59	85.53	86.67