Design of an Image Content Understanding and Information Extraction Algorithm Integrating Natural Language Processing

With the rapid development of artificial intelligence (AI) technologies, the integration of image content understanding and natural language processing (NLP) has become a hot research topic in the fields of computer vision and NLP.Image content understanding requires not only image classification and object detection capabilities but also the ability to perform in-depth semantic analysis and expression of complex information within the image.Advances in NLP have enabled computers to generate natural language descriptions related to the content of images, thereby facilitating cross-modal communication.In recent years, end-to-end image content understanding methods and NLP-based image information extraction algorithms have gradually become essential technologies for solving multimodal learning and information extraction problems.However, existing methods still have certain limitations in terms of multimodal fusion, information transfer accuracy, and contextual understanding, especially when handling complex scenes and applications where system robustness and accuracy often fail to meet practical requirements.To address these issues, this paper proposes an image content understanding and information extraction algorithm design that integrates NLP.The main contributions of this paper are twofold: first, a deep learning-based end-to-end image content understanding method is proposed, which can directly extract efficient features from images and generate accurate natural language descriptions; second, an NLP-integrated image content information extraction method is introduced, which achieves more efficient and precise multimodal information extraction through deep coupling of image and text information.Experimental results show that the proposed methods significantly improve the accuracy and efficiency of image description generation and information extraction tasks, providing strong support for the deep integration of images and language.

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Publication Details

Journal
Traitement du signal
Published
2024-12-31
DOI
https://doi.org/10.18280/ts.410606
Citations
2
Primary Topic
Image Retrieval and Classification Techniques
Type
article
Field-Weighted Citation Impact
0.50
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article

Design of an Image Content Understanding and Information Extraction Algorithm Integrating Natural Language Processing

Aihua Li, Ling Pang
2 citations
Traitement du signal
Image Retrieval and Classification Techniques
0.50
article

Design of an Image Content Understanding and Information Extraction Algorithm Integrating Natural Language Processing

Aihua Li, Ling Pang
article en
2 citations

Abstract

With the rapid development of artificial intelligence (AI) technologies, the integration of image content understanding and natural language processing (NLP) has become a hot research topic in the fields of computer vision and NLP.Image content understanding requires not only image classification and object detection capabilities but also the ability to perform in-depth semantic analysis and expression of complex information within the image.Advances in NLP have enabled computers to generate natural language descriptions related to the content of images, thereby facilitating cross-modal communication.In recent years, end-to-end image content understanding methods and NLP-based image information extraction algorithms have gradually become essential technologies for solving multimodal learning and information extraction problems.However, existing methods still have certain limitations in terms of multimodal fusion, information transfer accuracy, and contextual understanding, especially when handling complex scenes and applications where system robustness and accuracy often fail to meet practical requirements.To address these issues, this paper proposes an image content understanding and information extraction algorithm design that integrates NLP.The main contributions of this paper are twofold: first, a deep learning-based end-to-end image content understanding method is proposed, which can directly extract efficient features from images and generate accurate natural language descriptions; second, an NLP-integrated image content information extraction method is introduced, which achieves more efficient and precise multimodal information extraction through deep coupling of image and text information.Experimental results show that the proposed methods significantly improve the accuracy and efficiency of image description generation and information extraction tasks, providing strong support for the deep integration of images and language.

Traitement du signalVol. 41(6)
Baoding University (CN), Hebei Finance University (CN)
Quality Education
Openalex Percentile: Top 32%
Image Retrieval and Classification Techniques
0.50
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