• Journal of the Korean Society of Clothing and Textiles
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• v.45 no.1
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• pp.200-216
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• 2021

This article considers short-form fashion videos as distinguished from fashion films, defines the concept, details the expressive characteristics of short-form fashion video, and reveals the method of producing it. For the methodology, a literature review was conducted to derive the concept and expression techniques. A case study was also performed to define the expressive characteristics. Five short-form fashion videos were also produced based on the results. The final results are as follows. First, short-form fashion video was defined as a fashion medium on the purpose of fashion communication within 60 seconds and classified by three digital image formats. Second, the result of analyzing the expression of the short-form fashion video shows the simplicity and reconstitution, characterization and remediation, borderless and expansion, and synesthesia trigger of the fashion image. Third, five short-form fashion videos were produced based on the theme of the digital garden. It shows that the short-form fashion video intensively expresses the content as a medium whose sensational expression is more prominent than the composition of the story by the short running time that reflects the taste of digital mainstream.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.329-346
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• 2022

Recently, as the market for short form videos (Instagram, TikTok, YouTube) on social media has gradually increased, research using them is actively being conducted in the artificial intelligence field. A representative research field is Video to Shop, which detects fashion products in videos and searches for product images. In such a video-based artificial intelligence model, product features are extracted using convolution operations. However, due to the limitation of computational resources, extracting features using all the frames in the video is practically impossible. For this reason, existing studies have improved the model's performance by sampling only a part of the entire frame or developing a sampling method using the subject's characteristics. In the existing Video to Shop study, when sampling frames, some frames are randomly sampled or sampled at even intervals. However, this sampling method degrades the performance of the fashion product search model while sampling noise frames where the product does not exist. Therefore, this paper proposes a sampling method MF (Missing Fashion items on frame) sampler that removes noise frames and improves the performance of the search model. MF sampler has improved the problem of resource limitations by developing a keyframe mechanism. In addition, the performance of the search model is improved through noise frame removal using the noise detection model. As a result of the experiment, it was confirmed that the proposed method improves the model's performance and helps the model training to be effective.