Feature Fusion-Based Re-Ranking for Home Textile Image Retrieval

Ziyi Miao, Lan Yao (), Feng Zeng (), Yi Wang and Zhiguo Hong
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Ziyi Miao: School of Computer Science and Engineering, Central South University, Changsha 410083, China
Lan Yao: School of Mathematics, Hunan University, Changsha 410082, China
Feng Zeng: School of Computer Science and Engineering, Central South University, Changsha 410083, China
Yi Wang: Raycloud Technology Company, Hangzhou 310052, China
Zhiguo Hong: Raycloud Technology Company, Hangzhou 310052, China

Mathematics, 2024, vol. 12, issue 14, 1-20

Abstract: In existing image retrieval algorithms, negative samples often appear at the forefront of retrieval results. To this end, in this paper, we propose a feature fusion-based re-ranking method for home textile image retrieval, which utilizes high-level semantic similarity and low-level texture similarity information of an image and strengthens the feature expression via late fusion. Compared with single-feature re-ranking, the proposed method combines the ranking diversity of multiple features to improve the retrieval accuracy. In our re-ranking process, Markov random walk is used to update the similarity metrics, and we propose local constraint diffusion based on contextual similarity. Finally, the fusion–diffusion algorithm is used to optimize the sorted list via combining multiple similarity metrics. We set up a large-scale home textile image dataset, which contains 89 k home textile product images from 12 k categories, and evaluate the image retrieval performance of the proposed model with the Recall@k and mAP@K metrics. The experimental results show that the proposed re-ranking method can effectively improve the retrieval results and enhance the performance of home textile image retrieval.

Keywords: home textile image retrieval; feature fusion; similarity diffusion; fusion diffusion; local constraint diffusion (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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