Texture classification in aerial photographs using multiscale and multilayer complex networks

2021 ◽  
Author(s):  
M.N. Favorskaya ◽  
A.N. Zhukovskaya
Keyword(s):  
Complex Networks ◽  
Texture Analysis ◽  
Complex Network ◽  
Multilayer Structure ◽  
Statistical Approach ◽  
Texture Classification ◽  
Classification Method ◽  
Aerial Photographs ◽  
Functional Connections

Texture classification using oriented complex networks considers the functional connections between topological elements and simulates the complex textures more accurately. In contrast to the classical spatial texture analysis, we offer a novel function of weights in complex networks and a classification method that takes into account the scaling and color of textures. For this, three complex networks represented R, G and B components are built, which provide invariance of color aerial photographs obtained at different times. Comparison of the classification results using the proposed multiscale complex networks and conventional texture analysis based on a statistical approach is given. Also we extended this approach on color aerial photographs using multilayer structure of complex network.

scholarly journals Optimal filter bank representation in texture classification using support vector machines

2021 ◽  
Author(s):  
Samsher Singh Sidhu
Keyword(s):  
Support Vector Machines ◽  
Texture Analysis ◽  
Classification Accuracy ◽  
Texture Classification ◽  
Classification Method ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Translation Invariant ◽  
Vector Machines ◽  
Wavelet Frame Transform

Texture analysis has been a field of study for over three decades in many fields including electrical engineering. Today, texture analysis plays a crucial role in many tasks ranging from remote sensing to medical imaging. Researchers in this field have dealt with many different approaches, all trying to achieve the goal of high classification accuracy. The main difficulty of texture analysis was the lack of ability of the tools to characterize adequately different scales of the textures effectively. The development in multi-resolution analysis such as Gabor and Wavelet Transform help to overcome this difficulty. This thesis describes the texture classification algorithm that uses the combination of statistical features and co-occurrence features of the Discrete Wavelet Transformed images. The classification accuracy is increased by using translation-invariant features generated from the Discrete Wavelet Frame Transform. The results are further improved by focussing on the transformed images used for feature extraction by using filters which essentially extract those areas of the image that discriminate themselves from other image classes. In effect, by reducing the spatial characteristics of images that contribute to the features, the texture classification method still has the ability to preserve the classification accuracy. Support Vector Machines has proved excellent performance in the area of pattern recognition problems. We have applied SVMs with the texture classification method described above and, when compared to traditional classifiers, SVM has produced more accurate classification results on the Brodatz texture album.

Information on evolutionary age in redundancy of complex network

2019 ◽  
Vol 33 (27) ◽  
pp. 1950331
Author(s):  
Shiguo Deng ◽  
Henggang Ren ◽  
Tongfeng Weng ◽  
Changgui Gu ◽  
Huijie Yang
Keyword(s):  
Principal Component Analysis ◽  
Complex Networks ◽  
Metabolic Network ◽  
Complex Network ◽  
Cellular Networks ◽  
Topological Structure ◽  
Quantitative Measure ◽  
Principal Component ◽  
Evolutionary Information ◽  
Local Patterns

Evolutionary processes of many complex networks in reality are dominated by duplication and divergence. This mechanism leads to redundant structures, i.e. some nodes share most of their neighbors and some local patterns are similar, called redundancy of network. An interesting reverse problem is to discover evolutionary information from the present topological structure. We propose a quantitative measure of redundancy of network from the perspective of principal component analysis. The redundancy of a community in the empirical human metabolic network is negatively and closely related with its evolutionary age, which is consistent with that for the communities in the modeling protein–protein network. This behavior can be used to find the evolutionary difference stored in cellular networks.

scholarly journals CNFE-SE: a novel approach combining complex network-based feature engineering and stacked ensemble to predict the success of intrauterine insemination and ranking the features

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Sima Ranjbari ◽  
Toktam Khatibi ◽  
Ahmad Vosough Dizaji ◽  
Hesamoddin Sajadi ◽  
Mehdi Totonchi ◽  
...  
Keyword(s):  
Complex Networks ◽  
Complex Network ◽  
Semen Quality ◽  
Intrauterine Insemination ◽  
Machine Learning Algorithms ◽  
Semen Parameters ◽  
Feature Engineering ◽  
Operating Characteristics ◽  
Scoring Method ◽  
Novel Approach

Abstract Background Intrauterine Insemination (IUI) outcome prediction is a challenging issue which the assisted reproductive technology (ART) practitioners are dealing with. Predicting the success or failure of IUI based on the couples' features can assist the physicians to make the appropriate decision for suggesting IUI to the couples or not and/or continuing the treatment or not for them. Many previous studies have been focused on predicting the in vitro fertilization (IVF) and intracytoplasmic sperm injection (ICSI) outcome using machine learning algorithms. But, to the best of our knowledge, a few studies have been focused on predicting the outcome of IUI. The main aim of this study is to propose an automatic classification and feature scoring method to predict intrauterine insemination (IUI) outcome and ranking the most significant features. Methods For this purpose, a novel approach combining complex network-based feature engineering and stacked ensemble (CNFE-SE) is proposed. Three complex networks are extracted considering the patients' data similarities. The feature engineering step is performed on the complex networks. The original feature set and/or the features engineered are fed to the proposed stacked ensemble to classify and predict IUI outcome for couples per IUI treatment cycle. Our study is a retrospective study of a 5-year couples' data undergoing IUI. Data is collected from Reproductive Biomedicine Research Center, Royan Institute describing 11,255 IUI treatment cycles for 8,360 couples. Our dataset includes the couples' demographic characteristics, historical data about the patients' diseases, the clinical diagnosis, the treatment plans and the prescribed drugs during the cycles, semen quality, laboratory tests and the clinical pregnancy outcome. Results Experimental results show that the proposed method outperforms the compared methods with Area under receiver operating characteristics curve (AUC) of 0.84 ± 0.01, sensitivity of 0.79 ± 0.01, specificity of 0.91 ± 0.01, and accuracy of 0.85 ± 0.01 for the prediction of IUI outcome. Conclusions The most important predictors for predicting IUI outcome are semen parameters (sperm motility and concentration) as well as female body mass index (BMI).

scholarly journals Topology Identification of Complex Network via Chaotic Ant Swarm Algorithm

2013 ◽  
Vol 2013 ◽  
pp. 1-5 ◽  
Author(s):  
Haipeng Peng ◽  
Lixiang Li ◽  
Jürgen Kurths ◽  
Shudong Li ◽  
Yixian Yang
Keyword(s):  
Complex Networks ◽  
Numerical Simulations ◽  
Complex Network ◽  
Parameter Optimization ◽  
Ant Algorithm ◽  
Structure Parameters ◽  
Topology Identification ◽  
Unknown Structure ◽  
Swarm Algorithm ◽  
Scientific Fields

Nowadays, the topology of complex networks is essential in various fields as engineering, biology, physics, and other scientific fields. We know in some general cases that there may be some unknown structure parameters in a complex network. In order to identify those unknown structure parameters, a topology identification method is proposed based on a chaotic ant swarm algorithm in this paper. The problem of topology identification is converted into that of parameter optimization which can be solved by a chaotic ant algorithm. The proposed method enables us to identify the topology of the synchronization network effectively. Numerical simulations are also provided to show the effectiveness and feasibility of the proposed method.

scholarly journals Local Derivative Vector Pattern: Hybrid Pattern for Content-Based Medical Image Retrieval

2020 ◽  
Vol 7 (4) ◽  
pp. 79-86
Author(s):  
Nagadevi Darapureddy ◽  
Nagaprakash Karatapu ◽  
Tirumala Krishna Battula
Keyword(s):  
Image Retrieval ◽  
Texture Analysis ◽  
Medical Image ◽  
Local Binary Pattern ◽  
Medical Images ◽  
Texture Classification ◽  
Medical Image Retrieval ◽  
Local Vector ◽  
Local Derivative ◽  
Local Derivative Pattern

This paper examines a hybrid pattern i.e. Local derivative Vector pattern and comparasion of this pattern over other different patterns for content-based medical image retrieval. In recent years Pattern-based texture analysis has significant popularity for a variety of tasks like image recognition, image and texture classification, and object detection, etc. In literature, different patterns exist for texture analysis. This paper aims at forming a hybrid pattern compared in terms of precision, recall and F1-score with different patterns like Local Binary Pattern (LBP), Local Derivative Pattern (LDP), Completed Local Binary Pattern (CLBP), Local Tetra Pattern (LTrP), Local Vector Pattern (LVP) and Local Anisotropic Pattern (LAP) which were applied on medical images for image retrieval. The proposed method is evaluated on different modalities of medical images. The results of the proposed hybrid pattern show biased performance compared to the state-of-the-art. So this can further extended with other pattern to form a hybrid pattern.

Differential Evolution Dynamic Analysis in the Form of Complex Networks

2016 ◽  
pp. 285-318 ◽  
Author(s):  
Lenka Skanderova ◽  
Ivan Zelinka
Keyword(s):  
Evolutionary Algorithms ◽  
Differential Evolution ◽  
Complex Networks ◽  
Dynamic Analysis ◽  
Evolutionary Algorithm ◽  
Complex Network ◽  
Novel Method

In this work, we investigate the dynamics of Differential Evolution (DE) using complex networks. In this pursuit, we would like to clarify the term complex network and analyze its properties briefly. This chapter presents a novel method for analysis of the dynamics of evolutionary algorithms in the form of complex networks. We discuss the analogy between individuals in populations in an arbitrary evolutionary algorithm and vertices of a complex network as well as between edges in a complex network and communication between individuals in a population. We also discuss the dynamics of the analysis.

Modern Diffusion of Products with Complex Network Models

2011 ◽  
pp. 119-133
Author(s):  
Atsushi Tanaka
Keyword(s):  
Complex Networks ◽  
Marketing Strategy ◽  
Complex Network ◽  
Network Models ◽  
Information Propagation ◽  
Multiagent Simulation ◽  
Product Marketing ◽  
Winner Takes All ◽  
Complex Network Models

In this chapter, some important matters of complex networks and their models are reviewed shortly, and then the modern diffusion of products under the information propagation using multiagent simulation is discussed. The remarkable phenomena like “Winner-Takes-All” and “Chasm” can be observed, and one product marketing strategy is also proposed.

scholarly journals Comparison of complex networks and tree-based methods of phylogenetic analysis and proposal of a bootstrap method

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e4349 ◽  
Author(s):  
Aristóteles Góes-Neto ◽  
Marcelo V.C. Diniz ◽  
Daniel S. Carvalho ◽  
Gilberto C. Bomfim ◽  
Angelo A. Duarte ◽  
...  
Keyword(s):  
Biological Sciences ◽  
Phylogenetic Analysis ◽  
Complex Networks ◽  
Complex Network ◽  
Bootstrap Method ◽  
Molecular Data ◽  
Data Sets ◽  
Fungal Cell ◽  
Taxonomic Groups ◽  
Inference Methods

Complex networks have been successfully applied to the characterization and modeling of complex systems in several distinct areas of Biological Sciences. Nevertheless, their utilization in phylogenetic analysis still needs to be widely tested, using different molecular data sets and taxonomic groups, and, also, by comparing complex networks approach to current methods in phylogenetic analysis. In this work, we compare all the four main methods of phylogenetic analysis (distance, maximum parsimony, maximum likelihood, and Bayesian) with a complex networks method that has been used to provide a phylogenetic classification based on a large number of protein sequences as those related to the chitin metabolic pathway and ATP-synthase subunits. In order to perform a close comparison to these methods, we selected Basidiomycota fungi as the taxonomic group and used a high-quality, manually curated and characterized database of chitin synthase sequences. This enzymatic protein plays a key role in the synthesis of one of the exclusive features of the fungal cell wall: the presence of chitin. The communities (modules) detected by the complex network method corresponded exactly to the groups retrieved by the phylogenetic inference methods. Additionally, we propose a bootstrap method for the complex network approach. The statistical results we have obtained with this method were also close to those obtained using traditional bootstrap methods.

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