Feature Fusion-Based Re-Ranking for Home Textile Image Retrieval
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Published:2024-07-11
Issue:14
Volume:12
Page:2172
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ISSN:2227-7390
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Container-title:Mathematics
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language:en
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Short-container-title:Mathematics
Author:
Miao Ziyi1, Yao Lan2ORCID, Zeng Feng1ORCID, Wang Yi3, Hong Zhiguo3
Affiliation:
1. School of Computer Science and Engineering, Central South University, Changsha 410083, China 2. School of Mathematics, Hunan University, Changsha 410082, China 3. Raycloud Technology Company, Hangzhou 310052, China
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 89k home textile product images from 12k 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.
Funder
National Science Foundation of China
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