基于BoF模型的多特征融合果蔬图像分类方法

doi:10.16180/j.cnki.issn1007-7820.2020.07.009

Abstract

Abstract:

In view of the problem that traditional BoF model cannot effectively use image color and texture to better express fruit and vegetable characteristics, this paper proposed a multi-feature fusion algorithm for fruit and vegetable image classification in BoF model. Firstly, the algorithm extracted and fused the color moments and SURF features of the images to form SURFC feature descriptors. Secondly, K-means clustering was performed on CLBP and SURFC features to generate a feature dictionary, and all features were quantized by using the feature dictionary. Finally, SVM was used to train the coding result to get the classifier and recognize it. Results of the experiment showed that the BoF model with fused color and texture features was significantly better than the BoF model with single feature or other feature fusion. The recognition rate was up to 94%, which was more suitable for fruit and vegetable image classification.

Key words: BoF model, SURF, fruits and vegetables recognition, feature fusion, CLBP, SVM

CLC Number:

TP391

ZHANG Zechen,JU Zhiyong. Multi-feature Fusion Fruit and Vegetable Image Classification Based on Bag of Feature Model[J].Electronic Science and Technology, 2020, 33(7): 41-45.

Figures/Tables 9

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References 17

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Approachs	人工拍摄图片
Approachs	k=100	k=300	k=500	k=100	k=300	k=500
HSV	64.25	79.36	81.75	39.15	50.47	53.04
ColMom	62.99	71.49	72.01	36.22	41.45	43.52
LBP	49.83	62.95	70.13	32.78	40.23	41.45
Gabor	52.46	55.49	62.15	40.23	48.29	50.15
HOG	62.26	62.48	64.26	42.56	47.29	53.87
SURF	71.09	74.78	76.76	49.82	53.11	53.35

Approachs	人工拍摄图片
Approachs	k=100	k=300	k=500	k=100	k=300	k=500
SURF+HSV	77.24	77.24	78.40	53.26	55.05	56.76
SURF+ColMom	87.01	89.95	89.68	65.64	68.65	68.71
SURF+LBP	76.76	78.74	80.11	53.97	55.76	56.12
SURF+Gabor	69.72	71.23	71.58	50.25	51.13	52.16
SURF+HOG	72.56	74.08	73.92	54.62	55.58	56.19
SURFC	89.21	90.98	91.71	69.79	70.53	72.59

Approachs	人工拍摄图片
Approachs	k=100	k=300	k=500	k=100	k=300	k=500
SURFC+LBP	88.45	89.95	90.37	67.93	68.51	71.58
SURFC+Gabor	80.56	82.13	83.45	59.95	62.89	68.78
SURFC+HOG	75.46	78.86	79.37	58.15	60.58	63.49

Approachs	人工拍摄图片			网络下载图片
Approachs	k=100	k=300	k=500	k=100	k=300	k=500
SURFC+CLBP_S	91.39	92.82	93.64	74.09	76.02	77.74
SURFC+CLBP_M	91.23	93.44	92.89	73.22	75.88	76.95
SURFC+CLBP_M/C	90.98	92.89	93.57	72.73	76.02	75.45
SURFC/CLBP_S_M/C	91.35	91.93	91.87	71.58	76.38	77.67
SURFC+CLBP_SM	91.05	91.87	92.62	73.22	77.38	77.95
SURFC+CLBP_SMC	91.73	93.23	94.48	72.51	76.52	77.74

Multi-feature Fusion Fruit and Vegetable Image Classification Based on Bag of Feature Model

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