细粒度民族服饰图像检索的全局-局部特征提取方法

民族服饰图像具有不同民族风格的服装款式、配饰和图案,导致民族服饰图像细粒度检索准确率较低.因此,文中提出细粒度民族服饰图像检索的全局-局部特征提取方法.首先,基于自定义的民族服饰语义标注,对输入图像进行区域检测,分别获得前景、款式、图案和配饰图像.然后在全卷积网络结构的基础上构建多分支的全局-局部特征提取模型,对不同区域的服饰图像进行特征提取,分别获得全局、款式、图案和配饰的卷积特征.最后,先对全局特征进行相似性度量,得到初步检索结果,再使用Top-50检索结果的局部特征与查询图像的局部特征进行重排序,优化排序并输出最终的检索结果.在构建的民族服饰图像数据集上的实验表明,文中方法有效提高民族服饰图像检索的准确率.

Global-Local Feature Extraction Method for Fine-Grained National Clothing Image Retrieval

The low accuracy of fine-grained retrieval of national clothing images is caused by different clothing styles, accessories and patterns of national clothing. To address is problem, a global-local feature extraction method for fine-grained clothing image retrieval is proposed. Firstly, the input image is detected to obtain the foreground, styles, accessories and patterns images based on semantic labels of national clothing. Secondly, a multi-branch feature extraction model based on fully convolutional network is constructed to extract features from clothing images in different regions and obtain convolutional features of global, styles, accessories and patterns. Finally, the preliminary retrieval results are obtained by applying a similarity measure to the global features. Then,re-ranking of the result is performed by the local features of top 50 retrieval results and the query image. The final retrieval results are output by the result of re-ranking. The experimental results on the constructed national clothing image dataset show that the proposed method improves the accuracy of national clothing image retrieval effectively.