Extended Multi-resolution Local Patterns - A Discriminative Feature Learning Approach for Colonoscopy Image Classification

2017 ◽  
pp. 48-58
Author(s):  
Siyamalan Manivannan ◽  
Emanuele Trucco
Keyword(s):  
Image Classification ◽  
Feature Learning ◽  
Learning Approach ◽  
Discriminative Feature ◽  
Local Patterns

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 Unsupervised Deep Discriminative Feature Learning for Aerial Image Classification

2017 ◽  
Vol 10 (8) ◽  
pp. 121-136
Author(s):  
Tao Shi ◽  
Chunlei Zhang ◽  
Hongge Ren ◽  
Fujin Li
Keyword(s):  
Image Classification ◽  
Feature Learning ◽  
Aerial Image ◽  
Discriminative Feature
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