A Survey on Fashion Image Retrieval

Author:

Islam Sk Maidul1ORCID,Joardar Subhankar2ORCID,Sekh* Arif Ahmed3ORCID

Affiliation:

1. Global Institute of Science & Technology, India

2. Haldia Institute of Technology, India

3. XIM University, India

Abstract

Fashion is the manner in which we introduce ourselves to the world and has become perhaps the biggest industry on the planet. In recent years, fashion-related research has received a lot of attention from computer vision researchers as a result of the growing demand by the fashion industry. Fashion image retrieval (FIR) is a difficult initiative and requires finding the right items from a huge collection of fashion items based on an image query. FIR has been applied successfully to clothing and footwear. Despite ongoing advances, FIR still suffers from limitations when applied to real-world visual endeavors. However, research on complex design items, for example, ornaments, has received less attention due to the complex nature of similarity and the unavailability of suitable datasets. This article presents a review of FIR and evaluation systems from different design datasets. The motivation behind this review is, to sum up the state-of-the-art procedures for retrieving fashion images for a given query image. In addition, we highlight promising directions for future research.

Publisher

Association for Computing Machinery (ACM)

Subject

General Computer Science,Theoretical Computer Science

Reference153 articles.

1. Kenan E. Ak, Ashraf A. Kassim, Joo Hwee Lim, and Jo Yew Tham. 2018. Learning attribute representations with localization for flexible fashion search. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 7708–7717.

2. Kenan Emir Ak, Joo Hwee Lim, Jo Yew Tham, and Ashraf Kassim. 2019. Semantically consistent hierarchical text to fashion image synthesis with an enhanced-attentional generative adversarial network. In Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision Workshop. IEEE, 3121–3124.

3. Kenan E. Ak, Joo Hwee Lim, Jo Yew Tham, and Ashraf A. Kassim. 2018. Efficient multi-attribute similarity learning towards attribute-based fashion search. In Proceedings of the 2018 IEEE Winter Conference on Applications of Computer Vision. IEEE, 1671–1679.

4. Kenan E. Ak Joo Hwee Lim Jo Yew Tham and Ashraf A. Kassim. 2018. Which shirt for my first date? towards a flexible attribute-based fashion query system. Pattern Recognition Letters 112 (2018) 212–218.

5. Alberto Baldrati, Marco Bertini, Tiberio Uricchio, and Alberto Del Bimbo. 2022. Effective conditioned and composed image retrieval combining CLIP-based features. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 21466–21474.

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