Genetic Programming for Automatic Global and Local Feature Extraction to Image Classification
Created by W.Langdon from
gp-bibliography.bib Revision:1.7975
- @InProceedings{Bi:2018:CEC,
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author = "Ying Bi and Mengjie Zhang and Bing Xue",
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title = "Genetic Programming for Automatic Global and Local
Feature Extraction to Image Classification",
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booktitle = "2018 IEEE Congress on Evolutionary Computation (CEC)",
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year = "2018",
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editor = "Marley Vellasco",
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address = "Rio de Janeiro, Brazil",
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month = "8-13 " # jul,
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publisher = "IEEE",
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keywords = "genetic algorithms, genetic programming",
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URL = "https://openaccess.wgtn.ac.nz/articles/conference_contribution/Genetic_Programming_for_Automatic_Global_and_Local_Feature_Extraction_to_Image_Classification/13884998",
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DOI = "doi:10.1109/CEC.2018.8477911",
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size = "8 pages",
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abstract = "Feature extraction is an essential process to image
classification. Existing feature extraction methods can
extract important and discriminative image features but
often require domain expert and human intervention.
Genetic Programming (GP) can automatically extract
features which are more adaptive to different image
classification tasks. However, the majority GP-based
methods only extract relatively simple features of one
type i.e. local or global, which are not effective and
efficient for complex image classification. In this
paper, a new GP method (GP-GLF) is proposed to achieve
automatically and simultaneously global and local
feature extraction to image classification. To extract
discriminative image features, several effective and
well-known feature extraction methods, such as HOG,
SIFT and LBP, are employed as GP functions in global
and local scenarios. A novel program structure is
developed to allow GP-GLF to evolve descriptors that
can synthesise feature vectors from the input image and
the automatically detected regions using these
functions. The performance of the proposed method is
evaluated on four different image classification data
sets of varying difficulty and compared with seven GP
based methods and a set of non-GP methods. Experimental
results show that the proposed method achieves
significantly better or similar performance than almost
all the peer methods. Further analysis on the evolved
programs shows the good interpretability of the GP-GLF
method.",
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notes = "WCCI2018",
- }
Genetic Programming entries for
Ying Bi
Mengjie Zhang
Bing Xue
Citations