Image retrieval of colored spun fabrics based on Transformer network feature fusion
Aiming at the complexity and anisotropy of the color and texture of colored spun fabrics images that are difficult to be accurately described by a single feature,a color spun fabrics image retrieval algorithm based on Transformer network feature fusion was proposed.First,the high-level semantic information of colored spun fabrics was extracted by using the convolutional neural network model;then,the third-order color moment features of the image wewere fused by using the Transformer network to make full use of the complementarity between the ＂ high-level semantic information＂ and the ＂ low-level semantic information＂ of colored spun fabrics for image retrieval.Then,the third-order color moment features were fused using the Transformer network to make full use of the complementary nature of＂high-level semantic information＂ and ＂low-level semantic information＂ of color textile images for image retrieval.In this paper,14 different types of color-spun knitted fabric sample images were used for retrieval,and the Top-10 recall and mAP of this system reach 98.35%and 89.25%,respectively,which is an improvement in the recall and mAP of retrieving Top-10 compared to a single network model incorporating an attention mechanism.
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