ABSTRACT:
As the image sharing websites like Flickr become more and more popular, extensive scholars
concentrate on tag-based image retrieval (TBIR). 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
paper, 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 REQUIREMENTS:
HARDWARE REQUIREMENTS:
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System : Pentium Dual Core.
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Hard Disk : 120 GB.
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Monitor : 15’’ LED
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Input Devices : Keyboard, Mouse
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Ram : 1 GB
SOFTWARE REQUIREMENTS:
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Operating system : Windows 7.
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Coding Language : JAVA/J2EE
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Tool : Netbeans 7.2.1
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Database : MYSQL
REFERENCE:
Yaxiong Wang, Li Zhu, Xueming Qian, Member, IEEE, Junwei Han, “Joint Hypergraph Learning for Tag-based Image Retrieval”, IEEE
Transactions on Image Processing, 2018.