Development of a hat style recognition system based on image processing and machine learning

The purpose of this paper is to investigate the recognition mechanism of hat styles and develop a corresponding hat style recognition system (HSRS). An image processing and machine learning integrated method (IPML) is proposed and validated for automatic hat style recognition. First, 4 kinds of hat styles (borderless knitted hats, berets, top hats and peaked hats) with 800 pictures are employed as research objects and divided into two categories: the first 400 serve as the training set and the rest 400 as the test set. Then, IPML is proposed to obtain a hat silhouette. Curvature feature points are extracted from hat silhouette and further used as parameters for the automatic hat style recognition. In the real recognition process, a new case is compared with the pre-set 400 samples in the training set regarding these characteristic parameters. A Hausforff distance-based similarity measurement tool is used in the comparison process. The experimental results show that when the curvature feature points are 70 and the output results are 3, the average recognition accuracy rate can reach 90.5%, of which the value of borderless knitted hats is the highest with 98% and followed by the top hats with 95%. This work can be used for hat recommendation systems. It can also be extended to support the area of personalized industrial product design such as fashion design, furniture design and advertisement design.