Image retrieval based on Multi Expression Programming algorithms

The effectiveness of content-based image retrieval (CBIR) systems can be improved by combining image features or by weighting image similarities, as computed from multiple feature vectors. However, feature combination does not always make sense and the combined similarity function can be more comple...

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Bibliographic Details
Published in:Proceedings (International Conference on Natural Computation. Print) pp. 1359 - 1364
Main Authors: Wang, Weihong, Lin, Wenrou, Li, Qu
Format: Conference Proceeding Journal Article
Language:English
Published: IEEE 01.07.2013
Subjects:
Analogies
Biological cells
Computation
Design engineering
Feature extraction
Image descriptors
Image retrieval
Mathematical analysis
Mathematical models
MEP Model
Programming
Retrieval
Shape
Vectors
ISSN:2157-9555, 2157-9563
Online Access:Get full text
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Summary:The effectiveness of content-based image retrieval (CBIR) systems can be improved by combining image features or by weighting image similarities, as computed from multiple feature vectors. However, feature combination does not always make sense and the combined similarity function can be more complex than weight-based functions to better satisfy the users' expectations. This paper addressed this problem by presenting a Multi-Expression Programming (MEP) framework to design combined similarity functions. This method allows nonlinear combination of image similarities and is validated through experiments, where the images are retrieved based on the shape of their objects. Experimental results demonstrate that the MEP framework is suitable for the design of effective combinations functions.
Bibliography:ObjectType-Article-2
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SourceType-Conference Papers & Proceedings-2
ISSN:2157-9555
2157-9563
DOI:10.1109/ICNC.2013.6818191