An Automatic Feature Extraction Approach to Image Classification Using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.7964

@InProceedings{Bi:2018:evoApplications,

• author = "Ying Bi and Bing Xue and Mengjie Zhang",
• title = "An Automatic Feature Extraction Approach to Image Classification Using Genetic Programming",
• booktitle = "21st International Conference on the Applications of Evolutionary Computation, EvoIASP 2018",
• year = "2018",
• editor = "Stefano Cagnoni and Mengjie Zhang",
• series = "LNCS",
• volume = "10784",
• publisher = "Springer",
• pages = "421--438",
• address = "Parma, Italy",
• month = "4-6 " # apr,
• organisation = "Species",
• keywords = "genetic algorithms, genetic programming, Image classification, Feature extraction, Image analysis",
• isbn13 = "978-3-319-77537-1",
• DOI = "doi:10.1007/978-3-319-77538-8_29",
• abstract = "Feature extraction is an essential process for image data dimensionality reduction and classification. However, feature extraction is very difficult and often requires human intervention. Genetic Programming (GP) can achieve automatic feature extraction and image classification but the majority of existing methods extract low-level features from raw images without any image-related operations. Furthermore, the work on the combination of image-related operators/descriptors in GP for feature extraction and image classification is limited. This paper proposes a multi-layer GP approach (MLGP) to performing automatic high-level feature extraction and classification. A new program structure, a new function set including a number of image operators/descriptors and two region detectors, and a new terminal set are designed in this approach. The performance of the proposed method is examined on six different data sets of varying difficulty and compared with five GP based methods and 42 traditional image classification methods. Experimental results show that the proposed method achieves better or comparable performance than these baseline methods. Further analysis on the example programs evolved by the proposed MLGP method reveals the good interpretability of MLGP and gives insight into how this method can effectively extract high-level features for image classification.",
• notes = "EvoApplications2018 held in conjunction with EuroGP'2018 EvoCOP2018 and EvoMusArt2018 http://www.evostar.org/2018/cfp_evoapps.php",

}

Genetic Programming entries for Ying Bi Bing Xue Mengjie Zhang

Citations