Fusion of complex networks and randomized neural networks for texture analysis

Ribas, Lucas Correia; Sá, Jarbas Joaci de Mesquita; Scabini, Leonardo F. S.; Bruno, Odemir Martinez

doi:10.1016/j.patcog.2019.107189

Cited by 28 publications

(30 citation statements)

References 64 publications

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“…To address these problems, we present in this paper a DT method that extends the approach proposed in [41] for static textures, that is, it combines a graph based description from the Complex Network (CN) framework, and a learned representation from the Randomized Neural Network (RNN) model.…”

Section: Discrimination-based Methods Generally Use Local Features Such As the Localmentioning

confidence: 99%

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Learning graph representation with Randomized Neural Network for dynamic texture classification

Ribas

Sá

Manzanera

et al. 2022

Applied Soft Computing

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Section: Discrimination-based Methods Generally Use Local Features Such As the Localmentioning

confidence: 99%

“…In this section, we describe the CPNN approach, which extends to dynamic textures the static texture characterization approach proposed in [41]. In the first step, the dynamic texture video is modeled into two directed graphs: spatial and temporal graphs.…”

Section: Proposed Approachmentioning

confidence: 99%

Learning graph representation with Randomized Neural Network for dynamic texture classification

Ribas

Sá

Manzanera

et al. 2022

Applied Soft Computing

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“…Texture is a key element of human visual perception, used in many computer vision systems and for a variety of applications [ 30 ]. In this step, we employed for feature extraction the following texture analysis techniques: Gray Level Difference Matrix (GLDM) [ 31 ], Fourier descriptors [ 32 ], Complex Network Texture Descriptor (CNTD) [ 33 ], Fractal descriptors [ 34 ], Adaptative Hybrid Pattern (AHP) [ 35 ], Local Binary Patters (LBP) [ 36 ], Complex Network and Randomized Neural Network (CNRNN) [ 37 ] and Local Complex Features and Neural Network (LCFNN) [ 37 ]. These techniques analyze texture information in different ways (using models, statistics, spectra, and learning) and are suitable for a small number of samples in the dataset, also providing fast results.…”

Section: Methodsmentioning

confidence: 99%

Electrochemical and optical detection and machine learning applied to images of genosensors for diagnosis of prostate cancer with the biomarker PCA3

Rodrigues

Soares

et al. 2021

Talanta

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The development of simple detection methods aimed at widespread screening and testing is crucial for many infections and diseases, including prostate cancer where early diagnosis increases the chances of cure considerably. In this paper, we report on genosensors with different detection principles for a prostate cancer specific DNA sequence (PCA3). The genosensors were made with carbon printed electrodes or quartz coated with layer-by-layer (LbL) films containing gold nanoparticles and chondroitin sulfate and a layer of a complementary DNA sequence (PCA3 probe). The highest sensitivity was reached with electrochemical impedance spectroscopy with the detection limit of 83 pM in solutions of PCA3, while the limits of detection were 2000 pM and 900 pM for cyclic voltammetry and UV–vis spectroscopy, respectively. That detection could be performed with an optical method is encouraging, as one may envisage extending it to colorimetric tests. Since the morphology of sensing units is known to be affected in detection experiments, we applied machine learning algorithms to classify scanning electron microscopy images of the genosensors and managed to distinguish those exposed to PCA3-containing solutions from control measurements with an accuracy of 99.9%. The performance in distinguishing each individual PCA3 concentration in a multiclass task was lower, with an accuracy of 88.3%, which means that further developments in image analysis are required for this innovative approach.

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“…towards the pixel of higher intensity. This idea was first employed in previous work for grayscale texture characterization [40], and here we extend its definitions for multilayer networks of color images, along with our new connection weight equation (Equation 4). Consider a network N ri = {V N , E N }, its set of edges is defined by…”

Section: Spatio-spectral Network (Ssn)mentioning

confidence: 99%

Spatio-spectral networks for color-texture analysis

Scabini

Ribas

Bruno

2020

Information Sciences

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Texture is one of the most-studied visual attribute for image characterization since the 1960s. However, most hand-crafted descriptors are monochromatic, focusing on the gray scale images and discarding the color information. In this context, this work focus on a new method for color texture analysis considering all color channels in a more intrinsic approach. Our proposal consists of modeling color images as directed complex networks that we named Spatio-Spectral Network (SSN). Its topology includes within-channel edges that cover spatial patterns throughout individual image color channels, while between-channel edges tackle spectral properties of channel pairs in an opponent fashion. Image descriptors are obtained through a concise topological characterization of the modeled network in a multiscale approach with radially symmetric neighborhoods. Experiments with four datasets cover several aspects of color-texture analysis, and results demonstrate that SSN overcomes all the compared literature methods, including known deep convolutional networks, and also has the most stable performance between datasets, achieving 98.5(±1.1) of average accuracy against 97.1(±1.3) of MCND and 96.8(±3.2) of AlexNet. Additionally, an experiment verifies the performance of the methods under different color spaces, where results show that SSN also has higher performance and robustness.Keywords color-texture · texture analysis · feature extraction · complex networks · spatio-spectral networks Complex NetworksThe researches in CNs arise from the combination of graph theory, physics, and statistics, with the aim of analyzing large networks that derive from complex natural processes. Initially, works have shown that there are structural patterns in most of these networks, something that is not expected in a random network. This led to the definition of CN models that allow us to understand the structural properties of real networks. The most popular models are the scale-free [5] and small-world [52] networks. Therefore, a new line of research has been opened for pattern recognition, where CNs are adopted as a tool for modeling and characterizing natural phenomena.The concepts of CN are applied in several areas such as physics, biology, nanotechnology, neuroscience, sociology, among others [13]. Applying CNs to some problem consists of two main steps: i) modeling the problem as a network; ii) structural analysis of the resulting CN. The topological quantification of the CN allows us to arrive at important conclusions related to the system that it represents. For example, local vertex measurements can highlight important network regions, estimate their vulnerability, find groups of similar vertices, and etc.

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Fusion of complex networks and randomized neural networks for texture analysis

Cited by 28 publications

References 64 publications

Learning graph representation with Randomized Neural Network for dynamic texture classification

Learning graph representation with Randomized Neural Network for dynamic texture classification

Electrochemical and optical detection and machine learning applied to images of genosensors for diagnosis of prostate cancer with the biomarker PCA3

Spatio-spectral networks for color-texture analysis

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