基于BoF模型的多特征融合果蔬图像分类方法

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

针对传统BoF模型无法有效利用图像颜色及纹理来更好地表述果蔬特征的问题,文中提出了一种在BoF模型中进行多特征融合的果蔬图像分类算法。该算法首先提取并融合图像的颜色矩和SURF特征形成SURFC特征描述子;然后分别对CLBP及SURFC特征进行K-均值聚类以生成特征词典,并使用特征词典对所有特征量化编码;最后使用SVM对编码结果进行训练得到分类器并识别。实验结果表明,BoF模型融合颜色和纹理特征后,在果蔬图像分类效果上明显优于单一特征或者其他特征融合的BoF模型,识别率最高可达到94%,更适合果蔬图像分类。

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.