# Methods and advancement of content-based fashion image retrieval: A Review

arXiv: https://arxiv.org/abs/2303.17371 · HTML: https://mlsanity.com/paper/2303.17371/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Mar 2023.

## Built on by (most cited first, 2 of 2)

- [LookBench: A Live and Holistic Open Benchmark for Fashion Image Retrieval](https://mlsanity.com/paper/2601.14706/) (2026)
- [Content-based jewellery item retrieval using the local region-based histograms](https://mlsanity.com/paper/2305.07540/) (2023)

## Abstract (arXiv)

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.