基于多特征融合的服装图像检索算法研究

Research on Clothing Image Retrieval Algorithm Based on Multi-feature Fusion

本文针对单个特征对服装图片内容表达不充分的问题,提出一种使用MobileNetV2模型并融合总和卷积池化(Sum-Pooled Convolutional,SPoC)算法、区域最大池化(Regional Maximum Activation of Convolutions,RMAC)算法和均值池化(General ized-Mean,GeM)算法提取服装特征的方法。在充分利用多个算法提取出有效的服装特征后,进行相似性度量后返回检索结果。试验结果表明,该方法可以结合多个算法提取出的多种特征信息,可以有效提高服装图像的检索性能。

Aiming at the problem that a single feature does not fully express the content of clothing images, this paper proposes a method to extract clothing features by using the MobileNetV2 model and integrating the Sum-Pooled Convolutional(SPoC) algorithm,the Regional Maximum Activation of Convolutions(RMAC) algorithm and the Generalized-Mean(GeM) algorithm. After making full use of multiple algorithms to extract effective clothing features, the Euclidean distance is used to obtain the similarity size and the similarity measurement is performed to return the retrieval result. The experimental results show that the method can combine various feature information extracted by multiple algorithms, and can effectively improve the retrieval performance of clothing images.