A Large Benchmark for Fabric Image Retrieval

Fabric image retrieval aims to match a probe fabric image from a gallery fabric image set, playing a great role in the textile industry. However, there is a lack of one comprehensive benchmark for fabric image retrieval. For this, a large benchmark for fabric image retrieval is proposed in this paper. Firstly, a dataset namely Fabric 1.0 is proposed, which contains 46,656 fabric images of 972 subjects. Each subject is collected with multiple images from various angles and both front and back sides. Secondly, a evaluation protocol is designed to evaluate the fabric image retrieval accuracy, which uses the cumulative match characteristic (CMC) curve and the mean average precision (MAP) as performance indicators. Finally, the baseline performance resulted from a non deep learning method (i.e., bag of feature) and three deep learning methods (i.e., LeNet, AlexNet and VGGNet) are reported and analyzed.