Fabric image retrieval based on GPQ semi-supervised neural network
To enhance the accuracy of fabric image retrieval,an improved semi-supervised neural network based on generalized product quantization(GPQ)is employed for efficient retrieval of weakly textured fabric images.The texture of the fabric image dataset is enhanced using the clip limited adaptive histogram equalization(CLAHE)method,thereby reinforcing the underlying texture features to reduce the probability of overfitting in deep learning features.Within the GPQ framework,which includes product quantization,a cosine similarity-based classifier,and compu-tation of the subspace minimum-maximum entropy loss,the extracted feature vectors were nor-malized to identify the most similar fabric images.The experimental fabric dataset comprises 1 800 images,representing 12 different texture styles of fabric samples.Comparative analyses were conducted with respect to the color histogram-based bag-of-words model,size-invariant feature transform model,nearest neighbors,and optimized product quantization algorithm.The results indicate that the improved GPQ semi-supervised neural network achieves a m AP value of 89.47％,demonstrating optimal retrieval performance.This method facilitates batch retrieval of similar fabric images at a low cost,and improves the accuracy of fabric image retrieval.
万方数据
维普
