A Gaussian Filter-Based Feature Learning Approach Using Genetic Programming to Image Classification

Created by W.Langdon from gp-bibliography.bib Revision:1.8129

@InProceedings{bi:2018:AJCAI,

• author = "Ying Bi and Bing Xue and Mengjie Zhang",
• title = "A Gaussian {Filter-Based} Feature Learning Approach Using Genetic Programming to Image Classification",
• booktitle = "Australasian Joint Conference on Artificial Intelligence",
• year = "2018",
• editor = "Tanja Mitrovic and Bing Xue and Xiaodong Li",
• volume = "11320",
• series = "LNCS",
• pages = "251--257",
• address = "Wellington, New Zealand",
• month = dec # " 11-14",
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming, ANN, Feature learning, Image classification, Gaussian filter, Evolutionary computation, Feature extraction",
• isbn13 = "978-3-030-03990-5",
• URL = "http://link.springer.com/chapter/10.1007/978-3-030-03991-2_25",
• DOI = "doi:10.1007/978-3-030-03991-2_25",
• abstract = "To learn image features automatically from the problems being tackled is more effective for classification. However, it is very difficult due to image variations and the high dimensionality of image data. This paper proposes a new feature learning approach based on Gaussian filters and genetic programming (GauGP) for image classification. Genetic programming (GP) is a well-known evolutionary learning technique and has been applied to many visual tasks, showing good learning ability and interpretability. In the proposed GauGP method, a new program structure, a new function set and a new terminal set are developed, which allow it to detect small regions from the input image and to learn discriminative features using Gaussian filters for image classification. The performance of GauGP is examined on six different data sets of varying difficulty and compared with four GP methods, eight traditional approaches and convolutional neural networks. The experimental results show GauGP achieves significantly better or similar performance in most cases.",

}

Genetic Programming entries for Ying Bi Bing Xue Mengjie Zhang

Citations