Dual attention mechanism networks for fabric image retrieval

Abstract

In recent years, with the booming development of deep learning, content using images has become a mainstream research hotspot for retrieval. Fabric image retrieval, as a specific application scenario for retrieval, has potential value in many areas such as textile product design, e-commerce and inventory management. However, in fabric retrieval, fabric image has unique striped texture features, and it is still a challenging task to better extract its features for retrieval. To address these problems, this paper proposes a method for fabric retrieval based on a dual attention mechanism. The method inserts a dual attention mechanism module into the convolutional neural network, firstly strip attention module, which connects long and distant image information to capture narrow feature regions, and secondly the channel attention module, which adaptively recalibrates the channel feature responses and selects the prominent and effective feature maps. Experimental results show that the proposed method performs superiorly in fabric image retrieval, and DAMNet is proposed to solve the problem of feature extraction and retrieval of such fabric images.