NDLI: Image retrieval technique using the clustering based on rearranged radon transform

Content Provider	Springer Nature Link
Author	An, Youngeun Lee, Jimin Park, Jongan
Copyright Year	2016
Abstract	This study proposed a new image retrieval technique in which the existing radon transform that was used for image retrieval is reinforced with noise invariance. For this, a radon transform was performed on an inquiry image which had been preprocessed to extract vector values and then the vector values were arranged depending on size to extract a second feature vector. After clustering and normalizing the levels of vector values based on the second feature vector, the feature vector was created. For a simulation on the image retrieval technique using the clustering based on rearranged radon transform, diverse images were used in this experiment. For performance analysis, the system proposed was compared with the retrieval system using a rearrangement hough transform based on voting number. As a result, the proposed image retrieval technique was more robust to geometric transforms such as rotated and scaled in the retrieval technique using the general radon transform and standard hough transform, and it had recall enhanced to 0.05 and precision enhanced to 0.04 in comparison with the rearrangement hough transform based on voting number.
Starting Page	12983
Ending Page	12997
Page Count	15
File Format	PDF
ISSN	13807501
Journal	Multimedia Tools and Applications
Volume Number	75
Issue Number	20
e-ISSN	15737721
Language	English
Publisher	Springer US
Publisher Date	2016-06-04
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Radon transform Clustering Rearranged Shape based image retrieval Multimedia Information Systems Computer Communication Networks Data Structures, Cryptology and Information Theory Special Purpose and Application-Based Systems
Content Type	Text
Resource Type	Article
Subject	Computer Networks and Communications Hardware and Architecture Software Media Technology

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