摘要
细粒度的图片分类是深度学习图片分类领域中的一个重要分支,其分类任务比一般的图片分类要困难,因为很多不同分类图片中的特征相似度极高,没有特别鲜明的特征用以区分,因而需要优化一个传统的图片分类方法.在一般的图片分类中,通常通过提取视觉以及像素级别的特征用来训练,然而直接应用到细粒度分类上并不太适配,效果仍有待提高,可考虑利用非像素级别的特征来加以区分.因此,我们提出联合文本信息和视觉信息作用于图片分类中,充分利用图片上的特征,将文本检测与识别算法和通用的图片分类方法结合,应用于细粒度图片分类中,在Con-text数据集上的实验结果表明我们提出的算法得到的准确率有显著的提升.
Fine-grained image classification is an important branch in the field of deep learning image classification.Since many different classified images are very similar in their features,and there is no particularly distinctive feature can be used to distinguish among them,it makes the classification task of fine-grained image more difficult than that of the general image.Therefore,a traditional image classification method needs to be optimized.Usually,visual and pixel-level features extraction is used in the training of the general image classification.However,direct application of this method to the fine-grained classification is not very suitable,and the effect still needs to be improved,while non-pixel-level features can be used to distinguish.Hence,we propose to combine text and visual information in the image classification,make full use of the features on the images,combine the text detection and recognition algorithms with general image classification methods,and apply it to the fine-grained image classification.In Con-text dataset,the experimental result shows that the accuracy obtained by the proposed algorithm has been significantly improved.
作者
姜倩
刘曼
JIANG Qian;LIU Man(Faculty of Information Technology,Beijing University of Technology,Beijing 100124,China)
出处
《计算机系统应用》
2020年第10期248-254,共7页
Computer Systems & Applications
