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

2018 ◽  
pp. 251-257 ◽  
Author(s):  
Ying Bi ◽  
Bing Xue ◽  
Mengjie Zhang
Keyword(s):  
Genetic Programming ◽  
Image Classification ◽  
Feature Learning ◽  
Learning Approach ◽  
Gaussian Filter

scholarly journals An effective feature learning approach using genetic programming with image descriptors for image classification

2020 ◽  
Author(s):  
Ying Bi ◽  
Bing Xue ◽  
Mengjie Zhang
Keyword(s):  
Genetic Programming ◽  
Image Classification ◽  
Learning Algorithm ◽  
Feature Learning ◽  
Classification Performance ◽  
Learning Approach ◽  
New Approach ◽  
Feature Descriptors ◽  
High Level ◽  
Almost All

© 2005-2012 IEEE. Being able to extract effective features from different images is very important for image classification, but it is challenging due to high variations across images. By integrating existing well-developed feature descriptors into learning algorithms, it is possible to automatically extract informative high-level features for image classification. As a learning algorithm with a flexible representation and good global search ability, genetic programming can achieve this. In this paper, a new genetic programming-based feature learning approach is developed to automatically select and combine five existing well-developed descriptors to extract high-level features for image classification. The new approach can automatically learn various numbers of global and/or local features from different types of images. The results show that the new approach achieves significantly better classification performance in almost all the comparisons on eight data sets of varying difficulty. Further analysis reveals the effectiveness of the new approach to finding the most effective feature descriptors or combinations of them to extract discriminative features for different classification tasks.

scholarly journals A gaussian filter-based feature learning approach using genetic programming to image classification

2021 ◽  
Author(s):  
Ying Bi ◽  
Bing Xue ◽  
Mengjie Zhang
Keyword(s):  
Genetic Programming ◽  
Image Classification ◽  
Feature Learning ◽  
Image Data ◽  
Input Image ◽  
Image Features ◽  
Learning Ability ◽  
Learning Approach ◽  
Gaussian Filters ◽  
Terminal Set

© Springer Nature Switzerland AG 2018. 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.

scholarly journals A gaussian filter-based feature learning approach using genetic programming to image classification

2021 ◽  
Author(s):  
Ying Bi ◽  
Bing Xue ◽  
Mengjie Zhang
Keyword(s):  
Genetic Programming ◽  
Image Classification ◽  
Feature Learning ◽  
Image Data ◽  
Input Image ◽  
Image Features ◽  
Learning Ability ◽  
Learning Approach ◽  
Gaussian Filters ◽  
Terminal Set

© Springer Nature Switzerland AG 2018. 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.

scholarly journals An Evolutionary Deep Learning Approach Using Genetic Programming with Convolution Operators for Image Classification

2021 ◽  
Author(s):  
Ying Bi ◽  
Bing Xue ◽  
Mengjie Zhang
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Genetic Programming ◽  
Image Classification ◽  
Feature Learning ◽  
Learning Approach ◽  
Data Sets ◽  
Convolution Operators ◽  
Program Structure ◽  
Terminal Set

© 2019 IEEE. Evolutionary deep learning (EDL) as a hot topic in recent years aims at using evolutionary computation (EC) techniques to address existing issues in deep learning. Most existing work focuses on employing EC methods for evolving hyper-parameters, deep structures or weights for neural networks (NNs). Genetic programming (GP) as an EC method is able to achieve deep learning due to the characteristics of its representation. However, many current GP-based EDL methods are limited to binary image classification. This paper proposed a new GP-based EDL method with convolution operators (COGP) for feature learning on binary and multi-class image classification. A novel flexible program structure is developed to allow COGP to evolve solutions with deep or shallow structures. Associated with the program structure, a new function set and a new terminal set are developed in COGP. The experimental results on six different image classification data sets of varying difficulty demonstrated that COGP achieved significantly better performance in most comparisons with 11 effectively competitive methods. The visualisation of the best program further revealed the high interpretability of the solutions found by COGP.

scholarly journals An Evolutionary Deep Learning Approach Using Genetic Programming with Convolution Operators for Image Classification

2021 ◽  
Author(s):  
Ying Bi ◽  
Bing Xue ◽  
Mengjie Zhang
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Genetic Programming ◽  
Image Classification ◽  
Feature Learning ◽  
Learning Approach ◽  
Data Sets ◽  
Convolution Operators ◽  
Program Structure ◽  
Terminal Set

© 2019 IEEE. Evolutionary deep learning (EDL) as a hot topic in recent years aims at using evolutionary computation (EC) techniques to address existing issues in deep learning. Most existing work focuses on employing EC methods for evolving hyper-parameters, deep structures or weights for neural networks (NNs). Genetic programming (GP) as an EC method is able to achieve deep learning due to the characteristics of its representation. However, many current GP-based EDL methods are limited to binary image classification. This paper proposed a new GP-based EDL method with convolution operators (COGP) for feature learning on binary and multi-class image classification. A novel flexible program structure is developed to allow COGP to evolve solutions with deep or shallow structures. Associated with the program structure, a new function set and a new terminal set are developed in COGP. The experimental results on six different image classification data sets of varying difficulty demonstrated that COGP achieved significantly better performance in most comparisons with 11 effectively competitive methods. The visualisation of the best program further revealed the high interpretability of the solutions found by COGP.

scholarly journals An effective feature learning approach using genetic programming with image descriptors for image classification

2020 ◽  
Author(s):  
Ying Bi ◽  
Bing Xue ◽  
Mengjie Zhang
Keyword(s):  
Genetic Programming ◽  
Image Classification ◽  
Learning Algorithm ◽  
Feature Learning ◽  
Classification Performance ◽  
Learning Approach ◽  
New Approach ◽  
Feature Descriptors ◽  
High Level ◽  
Almost All

© 2005-2012 IEEE. Being able to extract effective features from different images is very important for image classification, but it is challenging due to high variations across images. By integrating existing well-developed feature descriptors into learning algorithms, it is possible to automatically extract informative high-level features for image classification. As a learning algorithm with a flexible representation and good global search ability, genetic programming can achieve this. In this paper, a new genetic programming-based feature learning approach is developed to automatically select and combine five existing well-developed descriptors to extract high-level features for image classification. The new approach can automatically learn various numbers of global and/or local features from different types of images. The results show that the new approach achieves significantly better classification performance in almost all the comparisons on eight data sets of varying difficulty. Further analysis reveals the effectiveness of the new approach to finding the most effective feature descriptors or combinations of them to extract discriminative features for different classification tasks.

scholarly journals A One-shot Learning Approach to Image Classification using Genetic Programming

2020 ◽  
Author(s):  
Harith Al-Sahaf ◽  
Mengjie Zhang ◽  
M Johnston
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Genetic Programming ◽  
Image Classification ◽  
Local Binary Patterns ◽  
Support Vector ◽  
Learning Approach ◽  
Data Sets ◽  
Domain Specific ◽  
International Publishing

In machine learning, it is common to require a large number of instances to train a model for classification. In many cases, it is hard or expensive to acquire a large number of instances. In this paper, we propose a novel genetic programming (GP) based method to the problem of automatic image classification via adopting a one-shot learning approach. The proposed method relies on the combination of GP and Local Binary Patterns (LBP) techniques to detect a predefined number of informative regions that aim at maximising the between-class scatter and minimising the within-class scatter. Moreover, the proposed method uses only two instances of each class to evolve a classifier. To test the effectiveness of the proposed method, four different texture data sets are used and the performance is compared against two other GP-based methods namely Conventional GP and Two-tier GP. The experiments revealed that the proposed method outperforms these two methods on all the data sets. Moreover, a better performance has been achieved by Naïve Bayes, Support Vector Machine, and Decision Trees (J48) methods when extracted features by the proposed method have been used compared to the use of domain-specific and Two-tier GP extracted features. © Springer International Publishing 2013.

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