Joint Hypergraph Learning for Tag-based Image Retrieval

YEAR : 2021

Categories: , Tags: , ,


As the image sharing websites like Flickr become more and more popular, extensive scholars concentrate on tag-based image retrieval. It is one of the important ways to find images contributed by social users. In this research field, tag information and diverse visual features have been investigated. However, most existing methods use these visual features separately or sequentially. In this project, we propose a global and local visual features fusion approach to learn the relevance of images by hypergraph approach. A hypergraph is constructed first by utilizing global, local visual features, and tag information. Then, we propose a pseudo-relevance feedback mechanism to obtain the pseudo-positive images. Finally, with the hypergraph and pseudo relevance feedback, we adopt the hypergraph learning algorithm to calculate the relevance score of each image to the query. Experimental results demonstrate the effectiveness of the proposed approach.



System : Intel i3 and above
Hard Disk : 40GB
RAM : Minimum 4GB
Processer : 64-bit, four-core, 2.5 GHz minimum per core


Front End Language : HTML, CSS, JAVA, JSP SERVELTS
Backend : My SQL
Operating System : Windows 10 or 11


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