Texture recognition based on diffusion in networks

Gonçalves, Wesley Nunes; Silva, Núbia Rosa da; Costa, Luciano da Fontoura; Bruno, Odemir Martinez

doi:10.1016/j.ins.2016.04.052

Cited by 33 publications

(51 citation statements)

References 63 publications

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“…Another contribution is the use of diffusion in network to dynamic textures characterization, which provides a better characterization than other traditional network measures used in [26], improving the recognition task performance. This follows from the fact that directed diffusion has been used to highlight the diversity and separation of the dynamics in the respective network [24,11]. The dynamic textures are represented in directed complex networks and its activity associated to the spatial and temporal in-degree is used to compose the feature vector.…”

Section: Resultsmentioning

confidence: 99%

“…The walker conducts the walk according to Equation 4 until it visits a vertex without outgoing edge or the length of the walking is greater than a given threshold L. The activity α(v i ) of the vertex v i is defined as the number of visits received during the walks. To estimate more accurately the activity, M walks are started at each vertex of the network [24]. According to experimental evaluations in this paper, we define M = 50.…”

Section: Activity Estimation In Dynamic Texturesmentioning

confidence: 99%

“…The activity of the vertices describes important properties of the dynamic texture. In general, to characterize a static texture, a histogram of activity that correlates the activity of the vertex and its in-degree is used [24]. However, as mentioned before, in dynamic textures one of the challenges is to describe the appearance and motion properties.…”

Section: Composing the Feature Vector By The Activity Histogrammentioning

confidence: 99%

“…In this paper, we propose a new method for dynamic texture characterization based on diffusion in directed complex networks, following recent works that show that diffusion in directed networks may reveal clustering in the structure versus dynamics space [11,24]. The main contributions of this paper are the modeling of dynamic texture as a directed network and the feature extraction using diffusion from networks.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic texture analysis with diffusion in networks

Ribas

Gonçalves

Bruno

2019

Digital Signal Processing

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Dynamic texture is a field of research that has gained considerable interest from computer vision community due to the explosive growth of multimedia databases. In addition, dynamic texture is present in a wide range of videos, which makes it very important in expert systems based on videos such as medical systems, traffic monitoring systems, forest fire detection system, among others. In this paper, a new method for dynamic texture characterization based on diffusion in directed networks is proposed. The dynamic texture is modeled as a directed network. The method consists in the analysis of the dynamic of this network after a series of graph cut transformations based on the edge weights. For each network transformation, the activity for each vertex is estimated. The activity is the relative frequency that one vertex is visited by random walks in balance. Then, texture descriptor is constructed by concatenating the activity histograms. The main contributions of this paper are the use of directed network modeling and diffusion in network to dynamic texture characterization. These tend to provide better performance in dynamic textures classification. Experiments with rotation and interference of the motion pattern were conducted in order to demonstrate the robustness of the method. The proposed approach is compared to other dynamic texture methods on two very well know dynamic texture database and on traffic condition classification, and outperform in most of the cases.

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Section: Resultsmentioning

confidence: 99%

Section: Activity Estimation In Dynamic Texturesmentioning

confidence: 99%

Section: Composing the Feature Vector By The Activity Histogrammentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Dynamic texture analysis with diffusion in networks

Ribas

Gonçalves

Bruno

2019

Digital Signal Processing

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“…represents the pixel Euclidean distance. This same modeling approach is also used by the methods proposed in [19,20]. The connection weight inversely represent pixel similarity, where lower values means high similarity.…”

Section: Modeling Of Texture As Cnmentioning

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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Adaptive Evolution Threshold for Multiscale Dynamic Texture Classification Based on Complex Network

Liu

Qiao

2022

Advances in Intelligent Information Hiding and Multimedia Signal Processing

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Texture recognition based on diffusion in networks

Cited by 33 publications

References 63 publications

Dynamic texture analysis with diffusion in networks

Dynamic texture analysis with diffusion in networks

Spatio-spectral networks for color-texture analysis

Adaptive Evolution Threshold for Multiscale Dynamic Texture Classification Based on Complex Network

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