Dynamic Texture Recognition by Spatio-Temporal Multiresolution Histograms

Lu, Zongqing; Xie, Weixin; Pei, Jihong; Huang, Jianjun

doi:10.1109/acvmot.2005.44

Cited by 60 publications

(44 citation statements)

References 25 publications

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“…But they did not consider the multi-scale properties of DT. Lu et al proposed a new method using spatiotemporal multi-resolution histograms based on velocity and acceleration fields [21]. Velocity and acceleration fields of different spatiotemporal resolution image sequences were accurately estimated by the structure tensor method.…”

Section: Related Workmentioning

confidence: 99%

“…The methods based on optic flow [3], [4], [17], [18], [19], [20], [21], [22], [23], [24] are currently the most popular ones [5], because optic flow estimation is a computationally efficient and natural way to characterize the local dynamics of a temporal texture. Péteri and Chetverikov [3] proposed a method that combines normal flow features with periodicity features, in an attempt to explicitly characterize motion magnitude, directionality and periodicity.…”

Section: Related Workmentioning

confidence: 99%

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Dynamic Texture Recognition Using Local Binary Patterns with an Application to Facial Expressions

Zhao

Pietikäinen

2007

IEEE Trans. Pattern Anal. Mach. Intell.

2,493

1,625

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Abstract-Dynamic texture is an extension of texture to the temporal domain. Description and recognition of dynamic textures have attracted growing attention. In this paper, a novel approach for recognizing dynamic textures is proposed and its simplifications and extensions to facial image analysis are also considered. First, the textures are modeled with volume local binary patterns (VLBP), which are an extension of the LBP operator widely used in ordinary texture analysis, combining motion and appearance. To make the approach computationally simple and easy to extend, only the co-occurrences on three orthogonal planes (LBP-TOP) are then considered. A block-based method is also proposed to deal with specific dynamic events, such as facial expressions, in which local information and its spatial locations should also be taken into account. In experiments with two dynamic texture databases, DynTex and MIT, both the VLBP and LBP-TOP clearly outperformed the earlier approaches. The proposed block-based method was evaluated with the CohnKanade facial expression database with excellent results. Advantages of our approach include local processing, robustness to monotonic gray-scale changes and simple computation.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Dynamic Texture Recognition Using Local Binary Patterns with an Application to Facial Expressions

Zhao

Pietikäinen

2007

IEEE Trans. Pattern Anal. Mach. Intell.

2,493

1,625

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“…In recent years, recognizing dynamic textures has drawn increasing interests [1,2,3,4,5,6]. Dynamic texture recognition aims at finding dynamic visual patterns with repetitive motion, such as waving trees, flame, water waves, and traffic flow, in video.…”

Section: Introductionmentioning

confidence: 99%

“…The first category focuses on extracting local features, such as optical flow [2], local binary pattern (LBP) [5], and histogram [6], for recognition. Other information, such as temporal periodicity, can be jointly used for recognition.…”

Section: Introductionmentioning

confidence: 99%

Improving dynamic texture recognition with constraint subspace learning

Wang

Shi

2010

IEICE Electron. Express

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Abstract:We present a new framework for recognizing dynamic textures modeled by linear dynamic systems (LDS). The new framework improves previous methods by considering between-class subspace differences. Specifically, common part among different class subspaces is removed by projecting model subspaces onto a constraint subspace before recognition. This operation increases the separability among different classes of dynamic textures. The effectiveness of the new framework is shown by experimental results.

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“…The recent survey [6] mentions just a few methods that keep track of DT 'history' to a certain, limited temporal depth [3,11,21,22]. With small depth, one can only estimate short-term temporal variations such as acceleration [19]; no periodicity estimation is possible. Global spatiotemporal transforms like spatiotemporal wavelets [25] may become useful in multiscale periodicity analysis when motion periodicity appears at different scales (e.g., trunk, branches, leaves of a tree in the wind).…”

Section: Introductionmentioning

confidence: 99%

On motion periodicity of dynamic textures

Chetverikov

Fazekas

2006

Procedings of the British Machine Vision Conference 2006

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Dynamic textures (DTs) are videos of natural or artificial processes, such as waves, smoke, fire, or walking crowd. While activities and motion events produced by moving shapes are well localised in space and time, the spatiotemporal extent of most natural DTs is less definite. The notion of periodicity, or regularity, has been extensively studied in static texture analysis and in activity analysis, where it proved very useful. At the same time, much less attention has been paid to temporal (quasi-) periodicity of dynamic textures, despite the obvious fact that this property is inherent in dynamic texture. We discuss the reasons, then present a framework for quantitative motion periodicity analysis of DTs. Using the optic flow and adapting the SVD-based algorithm for signal period estimation [17], we measure degree of periodicity of natural dynamic textures. Numerous test results are presented, including the application of the temporal periodicity features to DT classification.

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Dynamic Texture Recognition by Spatio-Temporal Multiresolution Histograms

Cited by 60 publications

References 25 publications

Dynamic Texture Recognition Using Local Binary Patterns with an Application to Facial Expressions

Dynamic Texture Recognition Using Local Binary Patterns with an Application to Facial Expressions

Improving dynamic texture recognition with constraint subspace learning

On motion periodicity of dynamic textures

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