Deep learning-based multi-object association retrieval

Dong Cui; Zhihui Wang; Caixia Wang; Daoerji Fan; Huijuan Wu

doi:10.1117/12.2637763

13 May 2022 Deep learning-based multi-object association retrieval

Dong Cui, Zhihui Wang, Caixia Wang, Daoerji Fan, Huijuan Wu

Proceedings Volume 12248, Second International Conference on Sensors and Information Technology (ICSI 2022); 1224817 (2022) https://doi.org/10.1117/12.2637763
Event: 2nd International Conference on Sensors and Information Technology (ICSI 2022), 2022, Nanjing, China

Abstract

Advances in computer technology and artificial intelligence have led to a surge in the volume and complexity of multimedia data, and content-based image retrieval systems (CBIR) have become widely popular to extract useful information from this data. Traditional CBIR systems are implemented by inter-image features and cannot perform associative image retrieval. Therefore, in this paper, we propose a new method for associative image retrieval by mimicking the human brain mechanism. By using an improved deep residual network to extract different kinds of image features, and using a loss function to maximize the feature distance loss between non-associative image groups and minimize the loss between associative image groups, and finally using the trained model to achieve multi-objective associative retrieval.

Citation Download Citation

Dong Cui, Zhihui Wang, Caixia Wang, Daoerji Fan, and Huijuan Wu "Deep learning-based multi-object association retrieval", Proc. SPIE 12248, Second International Conference on Sensors and Information Technology (ICSI 2022), 1224817 (13 May 2022); https://doi.org/10.1117/12.2637763

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KEYWORDS

Convolution

Image retrieval

Feature extraction

Image enhancement

Image processing

Optimization (mathematics)

Databases

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