# Learning Deep Similarity Models with Focus Ranking for Fabric Image Retrieval

arXiv: https://arxiv.org/abs/1712.10211 · HTML: https://mlsanity.com/paper/1712.10211/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Dec 2017.

## Built on by (most cited first, 4 of 4)

- [Semantic Representation and Dependency Learning for Multi-Label Image Recognition](https://mlsanity.com/paper/2204.03795/) (2022)
- [Dual-Perspective Semantic-Aware Representation Blending for Multi-Label Image Recognition with Partial Labels](https://mlsanity.com/paper/2205.13092/) (2022)
- [Structural Textile Pattern Recognition and Processing Based on Hypergraphs](https://mlsanity.com/paper/2103.11271/) (2021)
- [SketchBodyNet: A Sketch-Driven Multi-faceted Decoder Network for 3D Human Reconstruction](https://mlsanity.com/paper/2310.06577/) (2023)

## Abstract (arXiv)

Fabric image retrieval is beneficial to many applications including clothing searching, online shopping and cloth modeling. Learning pairwise image similarity is of great importance to an image retrieval task. With the resurgence of Convolutional Neural Networks (CNNs), recent works have achieved significant progresses via deep representation learning with metric embedding, which drives similar examples close to each other in a feature space, and dissimilar ones apart from each other. In this paper, we propose a novel embedding method termed focus ranking that can be easily unified into a CNN for jointly learning image representations and metrics in the context of fine-grained fabric image retrieval. Focus ranking aims to rank similar examples higher than all dissimilar ones by penalizing ranking disorders via the minimization of the overall cost attributed to similar samples being ranked below dissimilar ones. At the training stage, training samples are organized into focus ranking units for efficient optimization. We build a large-scale fabric image retrieval dataset (FIRD) with about 25,000 images of 4,300 fabrics, and test the proposed model on the FIRD dataset. Experimental results show the superiority of the proposed model over existing metric embedding models.