Image retrieval based on multimodality neural network and local sensitive hash

Chen Chen

doi:10.1117/12.2680159

31 May 2023 Image retrieval based on multimodality neural network and local sensitive hash

Chen Chen

Proceedings Volume 12704, Eighth International Symposium on Advances in Electrical, Electronics, and Computer Engineering (ISAEECE 2023); 1270428 (2023) https://doi.org/10.1117/12.2680159
Event: 8th International Symposium on Advances in Electrical, Electronics and Computer Engineering (ISAEECE 2023), 2023, Hangzhou, China

Abstract

With the rapid development of deep convolutional neural networks, the use of deep convolutional neural networks to extract features instead of manual features has become one of the current research hotspots. However, deep convolutional neural network can not understand image features well, and there is a “semantic gap”. Contrastive Language-Image PreTraining (CLIP) model is a pre-training neural network model based on matching image and text. Use the pre-trained CLIP model to extract the high-dimensional feature vector of the image data set to be retrieved, and the Local Sensitive Hash (LSH) algorithm was used to extract the retrieval speed to complete the retrieval task based on the image content and text. Experimental results show that compared with other content-based image retrieval algorithms, the proposed algorithm can also understand the text information in the image to complete the retrieval task, and has a wider retrieval range.

Citation Download Citation

Chen Chen "Image retrieval based on multimodality neural network and local sensitive hash", Proc. SPIE 12704, Eighth International Symposium on Advances in Electrical, Electronics, and Computer Engineering (ISAEECE 2023), 1270428 (31 May 2023); https://doi.org/10.1117/12.2680159

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KEYWORDS

Image retrieval

Feature extraction

Data modeling

Image compression

Image processing

Semantics

Convolutional neural networks

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