摘要
本文以中国菜作为研究对象,提出了基于双线性模型的菜品识别方法.由于中国菜里很多菜品的相似性,导致分类的难度很大.本文借鉴了细粒度图像识别方法中的双线性模型,然后使用一种基于映射的方法得到一个更加低维的双线性特征表示.并在训练阶段采用一种大裕量softmax损失函数.该损失函数通过增加一个正整数变量,在损失函数里产生一个裕量,使同种类别的学习难度增加,从而使学到的特征更加有区分性.将网络在一个208类的中国菜数据集上的测试表明,与以往的方法相比,该方法提高了准确率,减少了过拟合,取得了更好的分类结果.
This article is based on bilinear model,focusing on Chinese food image recognition. The similarity between different Chinese food adds much difficulty for classification. Bilinear cnn model used for fine-grained classification is proved to be helpful in our experiment.Then a new projection algorithm is adopted to compress the high dimension of the bilinear vector. Moreover,this article implements a new loss function during training,which uses a large margin to make the samples in the same class converge harder. Validation on a 208 chinesefood dataset indicates that these two method integrated achieve a better result,not only improving the accuracy,but reducing overfitting.
引文
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