Research on Image Content Retrieval Technology Based on Visual Feature Extraction

Chengwei Bai

doi:10.47297/taposatWSP2633-456909.20240506

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Research on Image Content Retrieval Technology Based on Visual Feature Extraction

Volume 5, Issue 6

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Research on Image Content Retrieval Technology Based on Visual Feature Extraction
Research on Image Content Retrieval Technology Based on Visual Feature Extraction
Hohhot No.1 High School computer 33 Huanhe Street, Huimin District, Hohhot, Inner Mongolia, China
Vol. 5, Issue 6, Pages: 63-68(2024)
Published： 19 December 2024 ，
DOI：10.47297/taposatWSP2633-456909.20240506
Accepted：

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CHENGWEI BAI. (2024). Research on Image Content Retrieval Technology Based on Visual Feature Extraction. Theory and Practice of Science and Technology, 5(6), 63-68..
DOI：

CHENGWEI BAI. (2024). Research on Image Content Retrieval Technology Based on Visual Feature Extraction. Theory and Practice of Science and Technology, 5(6), 63-68.. DOI： 10.47297/taposatWSP2633-456909.20240506.

摘要

Abstract

Image content retrieval technology can quickly and accurately locate similar images in large-scale image databases by analyzing the visual features of images

and has become one of the core technologies for multimedia data management. This paper systematically reviews image content retrieval technology from the perspective of visual feature extraction

and deeply analyzes the application status and performance of low-level features (such as color

texture

shape)

mid-level features (such as edges and points of interest)

and high-level semantic feature extraction methods based on deep learning. Low-level and mid-level feature extraction methods can effectively describe the basic information of images and are suitable for image scenes with relatively simple structures. Deep learning models improve the retrieval accuracy in complex scenes through multi-layer feature expression capabilities and show strong generalization capabilities. This paper further discusses the main challenges faced by image content retrieval in visual feature extraction

including feature calculation overhead

adaptability to complex scenes

etc.

and points out the importance of improving feature extraction efficiency and retrieval system accuracy

providing a theoretical basis and practical reference for the development and application of related technologies.

关键词

Keywords

image content retrievalvisual feature extractiondeep learninglow-level featuresmid-level features

references

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