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Date Published: 30/05/2023
Online Published: 30/05/2023
Abstract Views: 117
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DOI: https://doi.org/10.54607/hcmue.js.20.5.3631(2023)
Issue
Vol. 20 No. 5 (2023)
Section
Articles
How to Cite
Lê , T. V. T., Nguyễn , T. Q. H., & Văn , T. T. (2023). IMAGE RETRIEVAL USING RS-TREE AND R-CNN DEEP LEARNING NETWORK. HCMUE Journal of Science, 20(5), 842. https://doi.org/10.54607/hcmue.js.20.5.3631(2023)

IMAGE RETRIEVAL USING RS-TREE AND R-CNN DEEP LEARNING NETWORK

Thị Vĩnh Thanh Lê , Thị Quỳnh Hương Nguyễn , Thế Thành Văn

Main Article Content

Abstract

 

In this paper, an image retrieval model using the RS-Tree and the Faster R-CNN deep learning network is proposed to enhance query performance. In this model, the following tasks are performed: (1) the RS-Tree is improved by the node separation algorithm to enhance the efficiency of the clustering vectors feature of the multi-object image set; (2) the R-CNN deep learning network is used to detect and classify objects in images; (3) bounding boxes containing objects in an image is extracted with low-level features and stored on the RS-Tree. For each input image, the system detects and classifies each object using the Faster R-CNN deep learning network, extracts low-level features of the image, and performs a process of image retrieval based on the RS-Tree. The experiment is performed on the MS-COCO multi-object image set of 5000 images with an accuracy of 77.39%. The experimental results are compared with related works to demonstrate the effectiveness of the proposed model.

 

Keywords

clustering, image Retrieval, R-CNN, RS-Tree

Article Details

References

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