Content-based fashion image retrieval (CBFIR) has been widely used in our daily life for searching fashion images or items from online platforms. In e-commerce purchasing, the CBFIR system can retrieve fashion items or products with the same or comparable features when a consumer uploads a reference image, image with text, sketch or visual stream from their daily life. This lowers the CBFIR system reliance on text and allows for a more accurate and direct searching of the desired fashion product. Considering recent developments, CBFIR still has limits when it comes to visual searching in the real world due to the simultaneous availability of multiple fashion items, occlusion of fashion products, and shape deformation. This paper focuses on CBFIR methods with the guidance of images, images with text, sketches, and videos. Accordingly, we categorized CBFIR methods into four main categories, i.e., image-guided CBFIR (with the addition of attributes and styles), image and text-guided, sketch-guided, and video-guided CBFIR methods. The baseline methodologies have been thoroughly analyzed, and the most recent developments in CBFIR over the past six years (2017 to 2022) have been thoroughly examined. Finally, key issues are highlighted for CBFIR with promising directions for future research.

翻译：基于内容的时装图像检索（CBFIR）已广泛应用于日常生活中，用于从在线平台搜索时装图像或商品。在电子商务购物中，当消费者上传参考图像、图文结合内容、手绘草图或日常生活中的视觉流时，CBFIR系统能够检索具有相同或相似特征的时装商品。这降低了CBFIR系统对文本的依赖，并实现了对所需时装商品更准确、更直接的搜索。然而，考虑到近期发展，由于现实中多件时装商品的同时存在、商品遮挡及形状形变等问题，CBFIR在真实世界视觉搜索方面仍存在局限性。本文聚焦于以图像、图文结合、手绘草图和视频为引导的CBFIR方法。据此，我们将CBFIR方法划分为四大类：图像引导型CBFIR（含属性和风格补充）、图像与文本引导型、手绘草图引导型以及视频引导型CBFIR方法。我们对基线方法进行了透彻分析，并深入审视了过去六年（2017年至2022年）CBFIR领域的最新进展。最后，我们指明了CBFIR的关键问题及具有前景的未来研究方向。