Fast Image Retrieval Method with Multi-feature Fusion Based on Graph Structure Statistical Modeling

CHEN Yizi; ZHAO Lijuan

doi:10.19554/j.cnki.1001-3563.2026.17.027

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Packaging Engineering ›› 2026, Vol. 47 ›› Issue (17) : 256-269. DOI: 10.19554/j.cnki.1001-3563.2026.17.027

Automatic and Intelligent Technology

Fast Image Retrieval Method with Multi-feature Fusion Based on Graph Structure Statistical Modeling

CHEN Yizi¹, ZHAO Lijuan^2,*

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Abstract

The work aims to propose a fast multi-feature fusion image retrieval method based on graph structure statistical modeling to address the drawbacks of traditional image retrieval methods, including insufficient representation of single-scale feature information and ineffective utilization of topological structure correlations, which lead to low retrieval accuracy, poor robustness, and difficulty in collaborative optimization of accuracy and efficiency. Firstly, an adaptive cross-layer feature pyramid was constructed, with the hierarchical weight learning mechanism and the redundancy filtering strategy introduced, to adaptively fuse shallow detailed information and deep semantic information, enhancing the model's discriminative capability for multi-scale objects. Subsequently, a dynamic topological modeling module was designed, which mapped multi-scale fused features into graph nodes, dynamically optimized edge weights combining feature similarity and spatial constraints, and aggregated local-global structural correlations through graph convolution, improving feature robustness. Finally, Principal Component Analysis was utilized to compress high-dimensional features and optimize feature dimensionality, further boosting retrieval efficiency while maintaining satisfactory accuracy. Meanwhile, a multi-dimensional statistical evaluation system for feature compactness was established, with inter-class and intra-class divergence ratio, feature dimensionality F-score, redundancy correlation coefficient and spectral energy distribution as quantitative indicators, to realize comprehensive evaluation of feature quality and unsupervised early prediction of retrieval performance. Experimental results on multiple datasets showed that the proposed method could complete feature extraction and retrieval for a single image within 12.3 ms. Its retrieval accuracy outperformed the state-of-the-art methods by 3.5%-3.8% on general datasets, and 4.2%-5.8% on fine-grained and industrial dedicated datasets. The compactness and separable representation capability of the fused features are significantly improved by the proposed method, which can effectively balance the accuracy, real-time performance and robustness of retrieval tasks in complex scenes.

Key words

image retrieval / multi-feature fusion / dynamic topology statistical analysis / cross-layer feature pyramid / feature compression

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CHEN Yizi, ZHAO Lijuan. Fast Image Retrieval Method with Multi-feature Fusion Based on Graph Structure Statistical Modeling[J]. Packaging Engineering. 2026, 47(17): 256-269 https://doi.org/10.19554/j.cnki.1001-3563.2026.17.027

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