scholarly journals Kansei Information Processing in Multimedia Applying Intelligent Soft Computing Techniques

2000 ◽  
Vol 4 (6) ◽  
pp. 428-436 ◽  
Author(s):  
Takehisa Onisawa ◽  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Information Processing ◽  
Soft Computing ◽  
Set Theory ◽  
Fuzzy Set Theory ◽  
Interactive Genetic Algorithm ◽  
Fuzzy Measures ◽  
Soft Computing Techniques ◽  
Genetic Algorithm Approach

This paper mentions the concept of Kansei information that must be dealt with in multimedia. Kansei information has subjectivity, ambiguity, vagueness and situation dependence. This piece of information is not dealt with by the conventional natural science techniques. This paper also introduces soft computing techniques such as a neural network model, fuzzy set theory, a fuzzy measures and fuzzy integrals model, and the interactive genetic algorithm approach that are applied to Kansei information processing or some related problems.

scholarly journals Performance comparisons of particle swarm optimization, echo state neural network and genetic algorithm for vegetation segmentation

2017 ◽  
Vol 7 (1.1) ◽  
pp. 184
Author(s):  
Rincy Merlin Mathew ◽  
S. Purushothaman ◽  
P. Rajeswari
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Soft Computing ◽  
Particle Swarm ◽  
Band 3 ◽  
Landsat 8 ◽  
Swarm Optimization ◽  
Landsat 7 ◽  
Performance Comparisons

This article presents the implementation of vegetation segmentation by using soft computing methods: particle swarm optimization (PSO), echostate neural network(ESNN) and genetic algorithm (GA). Multispectral image with the required band from Landsat 8 (5, 4, 3) and Landsat 7 (4, 3, 2) are used. In this paper, images from ERDAS format acquired by Landsat 7 ‘Paris.lan’ (band 4, band 3, Band 2) and image acquired from Landsat 8 (band5, band 4, band 3) are used. The soft computing algorithms are used to segment the plane-1(Near infra-red spectra) and plane 2(RED spectra). The monochrome of the two segmented images is compared to present performance comparisons of the implemented algorithms.

scholarly journals Soft Computing Tool Approach for Texture Classification Using Discrete Cosine Transform

2011 ◽  
pp. 12-16
Author(s):  
Pankaj H. Chandankhede
Keyword(s):  
Neural Network ◽  
Soft Computing ◽  
Learning Algorithm ◽  
Texture Classification ◽  
Fuzzy Model ◽  
Performance Comparison ◽  
Cosine Transform ◽  
Neuro Fuzzy ◽  
Soft Computing Techniques ◽  
Computing Models

Texture can be considered as a repeating pattern of local variation of pixel intensities. Cosine Transform (DCT) coefficients of texture images. As DCT works on gray level images, the color scheme of each image is transformed into gray levels. For classifying the images using DCT, two popular soft computing techniques namely neurocomputing and neuro-fuzzy computing are used. A feedforward neural network is used to train the backpropagation learning algorithm and an evolving fuzzy neural network to classify the textures. The soft computing models were trained using 80% of the texture data and the remaining was used for testing and validation purposes. A performance comparison was made among the soft computing models for the texture classification problem. In texture classification the goal is to assign an unknown sample image to a set of known texture classes. It is observed that the proposed neuro-fuzzy model performed better than the neural network.

scholarly journals Soft Computing: Optimization of Hybridized Neural Network Variant with Genetic Algorithm for Anomaly Detection

2019 ◽  
Vol 8 (9) ◽  
Author(s):  
Ismaila Wasiu Oladimeji ◽  
Ayodele Adefemi Lawrence ◽  
Omidiora E. Olusayo ◽  
Falohun Adeleye S
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Anomaly Detection ◽  
Soft Computing

scholarly journals Interactive Genetic Algorithm Oriented toward the Novel Design of Traditional Patterns

Information ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 36 ◽  
Author(s):  
Jian Lv ◽  
Miaomiao Zhu ◽  
Weijie Pan ◽  
Xiang Liu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Bp Neural Network ◽  
Surrogate Model ◽  
Mean Squared Error ◽  
Back Propagation ◽  
Interactive Evolutionary Computation ◽  
Interactive Genetic Algorithm ◽  
Novel Design ◽  
Fuzzy Interval

To create alternative complex patterns, a novel design method is introduced in this study based on the error back propagation (BP) neural network user cognitive surrogate model of an interactive genetic algorithm with individual fuzzy interval fitness (IGA-BPFIF). First, the quantitative rules of aesthetic evaluation and the user’s hesitation are used to construct the Gaussian blur tool to form the individual’s fuzzy interval fitness. Then, the user’s cognitive surrogate model based on the BP neural network is constructed, and a new fitness estimation strategy is presented. By measuring the mean squared error, the surrogate model is well managed during the evolution of the population. According to the users’ demands and preferences, the features are extracted for the interactive evolutionary computation. The experiments show that IGA-BPFIF can effectively design innovative patterns matching users’ preferences and can contribute to the heritage of traditional national patterns.

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