Electronic Science and Technology ›› 2020, Vol. 33 ›› Issue (7): 41-45.doi: 10.16180/j.cnki.issn1007-7820.2020.07.009

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

ZHANG Zechen,JU Zhiyong   

  1. School of Optical-Electrical and Computer Engineering,University of Shanghai for Science and Technology,Shanghai 200093, China
  • Received:2019-05-06 Online:2020-07-15 Published:2020-07-15
  • Supported by:
    National Natural Science Foundation of China(81101116)


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