DynTex: A comprehensive database of dynamic textures

Péteri, Renaud; Fazekas, Sándor; Huiskes, Mark J.

doi:10.1016/j.patrec.2010.05.009

Cited by 301 publications

(206 citation statements)

References 6 publications

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“…The feature vectors of the four methods are tested using 83 videos from DynTex [9], a database that provides high-quality videos of dynamic textures. These videos are divided into five classes, representative of different spatio-temporal dynamics.…”

Section: Resultsmentioning

confidence: 99%

“…Our goal is to study the influence of spatial or temporal variables in the decomposition scheme. For relevant testing purposes, we also aim at computing results on a large online dynamic texture dataset [9].…”

Section: Introductionmentioning

confidence: 99%

“…In section 4 the construction of texture feature descriptors for each decomposition scheme is presented. Finally, the relevance of each decomposition scheme is tested on a large dataset of dynamic textures [9] and numerical results are presented and discussed.…”

Section: Introductionmentioning

confidence: 99%

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A Comparison of Wavelet Based Spatio-temporal Decomposition Methods for Dynamic Texture Recognition

Dubois

Péteri

Ménard

2009

Pattern Recognition and Image Analysis

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Abstract. This paper presents four spatio-temporal wavelet decompositions for characterizing dynamic textures. The main goal of this work is to compare the influence of spatial and temporal variables in the wavelet decomposition scheme. Its novelty is to establish a comparison between the only existing method [11] and three other spatio-temporal decompositions. The four decomposition schemes are presented and successfully applied on a large dynamic texture database. Construction of feature descriptors are tackled as well their relevance, and performances of the methods are discussed. Finally, future prospects are exposed.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Comparison of Wavelet Based Spatio-temporal Decomposition Methods for Dynamic Texture Recognition

Dubois

Péteri

Ménard

2009

Pattern Recognition and Image Analysis

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“…All images and results are computed from DynTex [18], a dynamic texture database which provides a diverse high-quality DT. DynTex sequences come from natural scenes presenting a wide variety of moving process.…”

Section: Numerical Results: Static Versus Dynamic Decomposition (Sd Vmentioning

confidence: 99%

Dynamic Texture Extraction and Video Denoising

Lugiez

Ménard

El-Hamidi

2009

Advanced Concepts for Intelligent Vision Systems

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Abstract. According to recent works, introduced by Y.Meyer [1] the decomposition models based on Total Variation (TV) appear as a very good way to extract texture from image sequences. Indeed, videos show up characteristic variations along the temporal dimension which can be catched in the decomposition framework. However, there are very few works in literature which deal with spatio-temporal decompositions. Thus, we devote this paper to spatio-temporal extension of the spatial color decomposition model. We provide a relevant method to accurately catch Dynamic Textures (DT) present in videos. Moreover, we obtain the spatio-temporal regularized part (the geometrical component), and we distinctly separate the highly oscillatory variations, (the noise). Furthermore, we present some elements of comparison between several models in denoising purpose.

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“…Examples of four types of measures. The first column shows the frames extracted from three DT videos in DynTex [19] that transformed to gray scale. The second to fifth columns show the corresponding measures (5) - (8).…”

Section: Spatio-temporal Measurement Of Pixelsmentioning

confidence: 99%

Dynamic texture classification using dynamic fractal analysis

Quan

Ling

et al. 2011

2011 International Conference on Computer Vision

100

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In this paper, we developed a novel tool called dynamic fractal analysis for dynamic texture (DT) classification, which not only provides a rich description of DT but also has strong robustness to environmental changes. The resulting dynamic fractal spectrum (DFS) for DT sequences consists of two components: One is the volumetric dynamic fractal spectrum component (V-DFS) that captures the stochastic self-similarities of DT sequences as 3D volume datasets; the other is the multi-slice dynamic fractal spectrum component (S-DFS) that encodes fractal structures of DT sequences on 2D slices along different views of the 3D volume. Various types of measures of DT sequences are collected in our approach to analyze DT sequences from different perspectives. The experimental evaluation is conducted on three widely used benchmark datasets. In all the experiments, our method demonstrated excellent performance in comparison with state-of-the-art approaches.

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DynTex: A comprehensive database of dynamic textures

Cited by 301 publications

References 6 publications

A Comparison of Wavelet Based Spatio-temporal Decomposition Methods for Dynamic Texture Recognition

A Comparison of Wavelet Based Spatio-temporal Decomposition Methods for Dynamic Texture Recognition

Dynamic Texture Extraction and Video Denoising

Dynamic texture classification using dynamic fractal analysis

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