融合渐进训练策略的logo图像分类

经济全球化赋予了logo巨大的商业价值,随着计算机视觉领域的发展,为logo分类与识别提供了更广阔的应用领域.本文针对logo图像的分类识别,为了提高模型对logo图像分类的能力,基于logo图像整体特征不显著且数量众多的特点,提出了用细粒度图像分类的方法渐进式多粒度拼图训练(progressive multi-granularity training of jigsaw patches, PMG-Net)对logo图像数据集进行分类.通过拼图生成器生成包含不同粒度信息的输入图像,再引入渐进式多粒度训练模块融合不同粒度的特征,融合后的特征更注重图像之间的细微差别,使logo图像分类的效果有显著提高.在提取输入图像特征时采用LeakyReLU (leaky rectified linear unit)激活函数保留图像中的负值特征信息,并引入通道注意力机制,调整特征通道的权重,增强特征信息指导能力以改进模型的分类效果.实验结果表明,本文在logo图像数据集上的分类精确率优于传统的分类方法.本文通过融合多粒度特征的渐进训练策略以及随机拼图生成器的方法实现了对logo图像的高效分类,为解决lo...

Logo Image Classification Incorporating Progressive Training Strategies

Economic globalization has given logo a huge commercial value, and the development of the computer vision provides a broader application field for logo classification and recognition. This study considers that the overall features of logo images are not significant, and the number of images is large, and then it proposes progressive multi-granularity training of jigsaw patches (PMG-Net), a method of fine-grained image classification, to classify the logo image dataset, so as to improve the ability of the model to classify logo images. The input images containing different granularity information are generated by the jigsaw patch generator, and then the progressive multi-granularity training module is introduced to fuse the features of different granularities. The fused features pay more attention to the subtle differences between images so that the effect of logo image classification is significantly improved. The leaky rectified linear unit (LeakyReLU) activation function is used to retain the negative feature information in the image when the input image features are extracted, and the channel attention mechanism is introduced to adjust the weights of the feature channels, so as to enhance the feature information guiding ability and improve the classification effect of the model. The experimental results show that the classification accuracy of this study on the logo image dataset is better than that of traditional classification methods. This study achieves efficient classification of logo images by incorporating a progressive training strategy with multi-granularity features and a random jigsaw patch generator, which provides a new idea to solve the existing problems in logo image classification.