Plaid Fabric Image Retrieval Based on Wavelet Transform and SIFT Features

Abstract

This study presents a method for plaid fabric image retrieval that combines wavelet and SIFT features to address the challenges of accuracy and efficiency in fabric retrieval due to diverse fabric types. The process starts with cropping plaid fabric images and applying histogram equalization to improve brightness and contrast. Texture is enhanced using the Sobel operator, and the Haar wavelet transform extracts image high-frequency components in various directions. Wavelet features are then derived through histogram statistics. The SIFT algorithm is utilized to describe local features by capturing key points and directional information. A codebook aggregates these features from the fabric database, and VLAD encoding generates a vector for the image features, which is further reduced to 256 dimensions via PCA. A similarity-weighted fusion method combines the wavelet and SIFT features, achieving an mAP of 0.67 and an average retrieval time of 1.1 seconds per image. This method significantly enhances plaid fabric retrieval, aiding in fabric design and production.