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International Journal of Multimedia Information Retrieval
Erschienen in: International Journal of Multimedia Information Retrieval 1/2024

01.03.2024 | Regular Paper

Incremental image retrieval method based on feature perception and deep hashing

verfasst von: Kaiyang Liao, Jie Lin, Yuanlin Zheng, Keer Wang, Wen Feng

Erschienen in: International Journal of Multimedia Information Retrieval | Ausgabe 1/2024

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Abstract

How to propose an image retrieval algorithm with adaptable model and wide range of applications for large-scale datasets has become a critical technical problem in current image retrieval. This paper proposed an Incremental Image Retrieval Method Based on Feature Perception and Deep Hashing. The algorithm contains two important parts: the hash function learning part and the incremental hash code mapping part. Firstly, a module is designed called Feature Perception Module to obtain multi-scale global context-aware information. It also keeps the scale and shape of the final extracted deep features invariant. Then, a new incremental hash loss function is designed to maintain the similarity between the query image and the dataset image; the advantage of this is that it can reduce the time cost of updating the model. The experimental results show that the algorithm model can perform well in incremental image retrieval. It is shown that the algorithm can solve the current problem of low retrieval efficiency and high cost due to retraining models caused by the dramatic increase in the number of images in the image retrieval field.
Vorheriger Artikel Deep multiple aggregation networks for action recognition
Nächster Artikel A new CNN-based semantic object segmentation for autonomous vehicles in urban traffic scenes

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Metadaten
Titel
Incremental image retrieval method based on feature perception and deep hashing
verfasst von
Kaiyang Liao
Jie Lin
Yuanlin Zheng
Keer Wang
Wen Feng
Publikationsdatum
01.03.2024
Verlag
Springer London
Erschienen in
International Journal of Multimedia Information Retrieval / Ausgabe 1/2024
Print ISSN: 2192-6611
Elektronische ISSN: 2192-662X
DOI
https://doi.org/10.1007/s13735-024-00319-7

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