Solving imbalanced dataset problems for high-dimensional image processing by swarm optimization

2016 ◽  
pp. 311-321 ◽  
Author(s):  
J. Li ◽  
S. Fong
Keyword(s):  
Image Processing ◽  
High Dimensional ◽  
Imbalanced Dataset ◽  
Swarm Optimization ◽  
Dimensional Image

High‐Dimensional Image Analysis using Histocytometry

2021 ◽  
Vol 1 (6) ◽  
Author(s):  
Luis Munoz‐Erazo ◽  
Alfonso J. Schmidt ◽  
Kylie M. Price
Keyword(s):  
Image Analysis ◽  
High Dimensional ◽  
Dimensional Image

scholarly journals Flexible and High Performance ASIPs for Pixel Level Image Processing and Two Dimensional Image Processing

2013 ◽  
Vol 21 (3) ◽  
pp. 552-562
Author(s):  
Hsuan-Chun Liao ◽  
Mochamad Asri ◽  
Tsuyoshi Isshiki ◽  
Dongju Li ◽  
Hiroaki Kunieda
Keyword(s):  
Image Processing ◽  
High Performance ◽  
Two Dimensional ◽  
Dimensional Image ◽  
Two Dimensional Image

A hybrid feature selection algorithm combining ReliefF and Particle swarm optimization for high-dimensional medical data

2021 ◽  
pp. 1-15
Author(s):  
Zhaozhao Xu ◽  
Derong Shen ◽  
Yue Kou ◽  
Tiezheng Nie
Keyword(s):  
Feature Selection ◽  
Particle Swarm Optimization ◽  
Random Forest ◽  
Classification Accuracy ◽  
Particle Swarm ◽  
Medical Data ◽  
High Dimensional ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Swarm Optimization

Due to high-dimensional feature and strong correlation of features, the classification accuracy of medical data is not as good enough as expected. feature selection is a common algorithm to solve this problem, and selects effective features by reducing the dimensionality of high-dimensional data. However, traditional feature selection algorithms have the blindness of threshold setting and the search algorithms are liable to fall into a local optimal solution. Based on it, this paper proposes a hybrid feature selection algorithm combining ReliefF and Particle swarm optimization. The algorithm is mainly divided into three parts: Firstly, the ReliefF is used to calculate the feature weight, and the features are ranked by the weight. Then ranking feature is grouped according to the density equalization, where the density of features in each group is the same. Finally, the Particle Swarm Optimization algorithm is used to search the ranking feature groups, and the feature selection is performed according to a new fitness function. Experimental results show that the random forest has the highest classification accuracy on the features selected. More importantly, it has the least number of features. In addition, experimental results on 2 medical datasets show that the average accuracy of random forest reaches 90.20%, which proves that the hybrid algorithm has a certain application value.

Feature Extraction in Remote Sensing High-Dimensional Image Data

2007 ◽  
Vol 4 (1) ◽  
pp. 107-111 ◽  
Author(s):  
Maciel Zortea ◽  
Victor Haertel ◽  
Robin Clarke
Keyword(s):  
Remote Sensing ◽  
Feature Extraction ◽  
Image Data ◽  
High Dimensional ◽  
Dimensional Image

Image Enhancement Techniques Using Particle Swarm Optimization Technique

2015 ◽  
pp. 327-347 ◽  
Author(s):  
V. Santhi ◽  
B. K. Tripathy
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
Particle Swarm Optimization ◽  
Real Time ◽  
Quality Enhancement ◽  
Swarm Optimization ◽  
Image Processing Techniques ◽  
Real Time Applications ◽  
High Level ◽  
Processing Techniques

The image quality enhancement process is considered as one of the basic requirement for high-level image processing techniques that demand good quality in images. High-level image processing techniques include feature extraction, morphological processing, pattern recognition, automation engineering, and many more. Many classical enhancement methods are available for enhancing the quality of images and they can be carried out either in spatial domain or in frequency domain. But in real time applications, the quality enhancement process carried out by classical approaches may not serve the purpose. It is required to combine the concept of computational intelligence with the classical approaches to meet the requirements of real-time applications. In recent days, Particle Swarm Optimization (PSO) technique is considered one of the new approaches in optimization techniques and it is used extensively in image processing and pattern recognition applications. In this chapter, image enhancement is considered an optimization problem, and different methods to solve it through PSO are discussed in detail.

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