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Digital image re-ranking algorithms directly retrieve images. Without text messages or images in these algorithms, a semantic gap between the visual feature and high-level semantics of the image exist, which will affect the sequencing performance of retrieval results. In this study, a digital image re-ranking algorithm based on multi-feature fusion was proposed to eliminate the effects of semantic gap on image re-ranking. This algorithm explored the deep relationship between the visual feature and semantic attributes of digital images by combining the background visual feature and semantic attributes. The mapping dictionary between visual features and semantic attributes was also constructed from the perspective of transition probability. The relevant weight components of visual feature in each dimension were calculated based on the query of context between the visual features in the expanded set and the relevance analysis of semantic attributes. Subsequently, the relevant fraction calculation formula was then established from diversity and relevance, and the relevant fraction of each digital image was sequenced. Finally, the proposed algorithm was verified by the comparison analysis using visual rank algorithm and pseudo-relevance feedback (PRF) algorithm. Results demonstrated that the non-interpolating average precision (AP) of the proposed algorithm in public databases Holiday, Oxford 5k, and Paris are 0.8456, 0.5412 and 0.7075, which are 2.4%, 3.29%, and 3.61%, and 0.86%, 4.24% and 6.4% higher than those of visual rank algorithm and PRF algorithm, respectively. The AP of the proposed algorithm increased significantly. Research results showed that the elimination of semantic gap could improve digital image re-ranking to some extent. Related conclusions could provide technical support to network-based image retrieval.

Analysis on Digital Image Re-Ranking Algorithm based on Multi-feature Fusion