An intelligent computational framework for the definition and identification of the womenswear silhouettes

The current studies on the clothing silhouette are very limited. The purpose of this paper is to propose an innovative framework to intelligently identify the womenswear silhouette with the latest computer technologies. To clearly define the womenswear silhouette, an accurate numerical definition is proposed.

The study first processes and segments the useful parts on the static catwalk image data files following existing graphic extraction approaches. Six basic alphabetical womenswear silhouette types are selected and numerically defined. Then, the proposed framework automatically classifies the six basic womenswear silhouettes considering the different slopes between three main clothing parts. Six clothing situations are discovered according to different designs and the detailed cases are systematically categorized. In addition, aspects influencing the judgment of the clothing silhouettes such as the skin, the background, the drastic change points are also considered. The proposed silhouette definition and identification framework is novel and proved accurate.

The proposed definition and identification framework of womenswear silhouettes have been proved a viable approach that is fully compatible with the current computer technologies. The validation study shows that the presented identification procedure has a desirable accuracy over 90 percent.

The proposed methodology develops brand new standards to numerically define and identify the womenswear silhouette, which was not available in the past. Besides, the measurement, the identification and the classification procedures are fully validated by the image data collected from 14 world famous brands over 11 consecutive seasons. It is shown that the proposed numerical framework of the womenswear silhouettes is a robust one, considering all of the observed design variabilities.