Fabric Image Retrieval Based on Fine-Grained Features

Abstract

Fabric image retrieval is crucial for textile mills to manage their inventory and samples, but it is challenging due to the diverse appearance and fine-grained texture of fabrics. This paper proposes an algorithm based on finegrained features to deal with this issue. The algorithm uses the coordinate attention(CA) module to extract precise location information of the fabric images and scales the overall network structure of Mobile Net V3 to reduce the training time and model parameters. The optimized model is selected based on the scaling factor method, and fabric retrieval experiments are conducted on the fabric image dataset(FID). The results show that the algorithm effectively improves the accuracy of fabric image feature extraction, with a retrieval accuracy(Acc) of 91. 82% and floating point operations(FLOPs) of 175. 34 MB. The Acc is improved by 13. 49 percentage points compared with that of the original Mobile Net V3 model, while the training time is reduced, and the inference speed is improved by 25. 14%. The algorithm has practical application value.

Keywords

fabric image retrieval / MobileNetV3 / fine-grained feature / attention mechanism / scaling factor

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Funding

National Key Research and Development Program of China(2020YFB1707700)