Complex networks-based texture extraction and classification method for mineral flotation froth images

Xu, Degang; Xiao, Chunxia; Xie, Yongfang; Yang, Chunhua; Gui, Weihua

doi:10.1016/j.mineng.2015.08.017

Cited by 42 publications

(16 citation statements)

References 14 publications

(20 reference statements)

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“…More recent and innovative techniques are approaching texture differently. For instance, there are texture descriptors based on image complexity analysis with fractal dimension [22,23], Complex Networks (CNs) [24,25,26,27,28] and Cellular Automaton [29]. Learning techniques are also gaining attention, for instance texture properties learned with a vocabulary of Scale Invariant Feature Transform (SIFT) [30], or using Convolutional Neural Networks (CNN) [31].Pure color methods are another kind of approach for color-texture analysis which considers only the image colors.…”

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confidence: 99%

Multilayer complex network descriptors for color–texture characterization

Scabini

Condori

Gonçalves

et al. 2019

Information Sciences

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A new method based on complex networks is proposed for color-texture analysis. The proposal consists on modeling the image as a multilayer complex network where each color channel is a layer, and each pixel (in each color channel) is represented as a network vertex. The network dynamic evolution is accessed using a set of modeling parameters (radii and thresholds), and new characterization techniques are introduced to capt information regarding within and between color channel spatial interaction. An automatic and adaptive approach for threshold selection is also proposed. We conduct classification experiments on 5 well-known datasets: Vistex, Usptex, Outex13, CURet and MBT. Results among various literature methods are compared, including deep convolutional neural networks with pre-trained architectures. The proposed method presented the highest overall performance over the 5 datasets, with 97.7 of mean accuracy against 97.0 achieved by the ResNet convolutional neural network with 50 layers. and its results, comparisons with literature methods and discussions; finally, on Section 5 we discuss the main findings and results of the work. Theoretical Concepts and ReviewOn this section, we present the theoretical concepts of color-texture and CN. Color-Texture AnalysisThe goal of texture analysis is to study how to measure texture aspects and employ it to image characterization. Although this approach has been explored for many years, most works approach gray-level images, i.e. a single color-channel representing the pixel luminance. On this case, the multispectral information is either discarded or is not present. However, nowadays images that derive from different sources are mostly colored, for instance from the internet, surveillance cameras, satellites, microscopes, personal cameras and much more. The recent increase in computer hardware performance also makes possible to analyze larger amounts of data, allowing to keep the color information for texture analysis.In general, color-texture methods found in the literature are mostly integrative, which separate color from texture. These methods usually compute traditional gray-level descriptors from each color-channel, separately. For that, various gray-level methods can be applied or combined into an integrative descriptor. There are many graylevel methods found in literature. Classical techniques can be divided into statistical, model-based and structural methods [15]. The statistical methods were one of the first approaches which considered texture as a property to characterize images. Among statistical methods, the most common ones are those based on gray-level co-occurrence matrices [16,17] and Local Binary Patterns (LBP) [18]. Model-based methods include descriptors such as Gabor filters [19], which explore texture in the frequency domain, and Markov random field models [20]. Structural methods consists on analyzing the texture as a combination of various smaller elements, that are spatially arranged to compose the overall texture pattern. This is achieved, for...

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Multilayer complex network descriptors for color–texture characterization

Scabini

Condori

Gonçalves

et al. 2019

Information Sciences

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“…The classification is carried out in the same way that the experienced plant operators make adjustments to the operating variables based on the observed froth visual characteristics (Moolman et al, 1995b;Xu et al, 2015).…”

Section: Classification Of Froth Imagesmentioning

confidence: 99%

Application of Image Processing and Adaptive Neuro-fuzzy System for Estimation of the Metallurgical Parameters of a Flotation Process

Jahedsaravani

Massinaei

Marhaban

2016

Chemical Engineering Communications

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It is a well-known fact in literature and practice that flotation froth features are closely related to process conditions and performance. The authors have already developed some reliable algorithms for measurement of the froth surface visual parameters like bubble size distribution, froth color, velocity and stability. Furthermore, the metallurgical parameters of a laboratory flotation cell were successfully predicted from the extracted froth features.In this research study the fuzzy c-mean clustering technique is utilized to classify the froth images (collected under different process conditions) based on the extracted visual characteristics. The classification of the images is actually necessary to determine the ideal froth structure and the target set-points for a machine vision control system. The results show that the captured froth images are well-classified into five categorizes on the basis of the extracted features. The correlation between the visual properties of froth (in different classes) and the metallurgical parameters is discussed and modeled by the Downloaded by [University of Sussex Library] at 13:54 28 June 20162 Adaptive Neuro-Fuzzy Inference System (ANFIS). The promising results illustrate that the performance of the existing batch flotation system can be satisfactorily estimated from the measured froth characteristics. Therefore, the outputs from the current machine vision system can be inputted to a process control system.

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“…An example is the set of techniques that use learning, such as descriptors based on a vocabulary of scale invariant feature transform (SIFT) [17], often called bag-of-visual-words (BOVW). Methods based on image complexity analysis are also gaining attention such as cellular automata [19] and complex networks (CN) [4,58,64,25,57]. In particular, methods based on the CN theory have achieved promising results due to its capacity to represent the relation among structural elements of texture.…”

Section: Introductionmentioning

confidence: 99%

Fusion of complex networks and randomized neural networks for texture analysis

Ribas

Sá

Scabini

et al. 2020

Pattern Recognition

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This paper presents a high discriminative texture analysis method based on the fusion of complex networks and randomized neural networks. In this approach, the input image is modeled as a complex networks and its topological properties as well as the image pixels are used to train randomized neural networks in order to create a signature that represents the deep characteristics of the texture. The results obtained surpassed the accuracies of many methods available in the literature. This performance demonstrates that our proposed approach opens a promising source of research, which consists of exploring the synergy of neural networks and complex networks in the texture analysis field.

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Complex networks-based texture extraction and classification method for mineral flotation froth images

Cited by 42 publications

References 14 publications

Multilayer complex network descriptors for color–texture characterization

Multilayer complex network descriptors for color–texture characterization

Application of Image Processing and Adaptive Neuro-fuzzy System for Estimation of the Metallurgical Parameters of a Flotation Process

Fusion of complex networks and randomized neural networks for texture analysis

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