A brief survey of dynamic texture description and recognition Dmitry Chetverikov1 and Renaud P´eteri2 1
2
Computer and Automation Institute, Budapest, Hungary
[email protected] Centre for Mathematics and Computer Science, Amsterdam, The Netherlands
[email protected]
1 Introduction Dynamic, or temporal, texture is a spatially repetitive, time-varying visual pattern that forms an image sequence with certain temporal stationarity. In dynamic texture (DT), the notion of self-similarity central to conventional image texture is extended to the spatiotemporal domain. DTs are typically videos of processes, such as waves, smoke, fire, a flag blowing in the wind, a moving escalator, or a walking crowd. In physics, there is a long-established tradition of measuring, quantifying and visualising fluid and other flows that can be viewed as special kinds of temporal textures. In particular, Particle Image Velocimetry [12] is a standard technique for making a flow visible and measurable by injecting many small particles that scatter light and show the fluid motion. A frame of a PIV sequence is a spatial texture; the whole sequence is a dynamic texture. Recently, there have been successful attempts to measure fluid flows using computer vision methods such as optic flow estimation [6] and feature tracking [5]. The mutual influence of physics and image processing is obvious. For these reasons, it would seem natural for the vision community to learn from physics how to mathematically describe processes and motion patterns presented as dynamic textures. However, in computer vision tasks and approaches are quite different from those in physics. In pattern recognition, the study of temporal textures dates back to early nineties when the pioneering paper by Nelson and Polana [18] was published. Nelson and Polana categorised visual motion into three classes [24]: activities, motion events and temporal textures. Activities, such as walking or digging, are defined as motion patterns that are periodic in time and localised in space. Motion events, like opening a door, do not show temporal or spatial periodicity. Finally, temporal textures exhibit statistical regularity but have indeterminate spatial and temporal extent. Computer vision aims at detection, segmentation and recognition of these three classes
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of visual motion, while in physics the emphasis is on the measurement and visualisation of physical processes. This paper is a brief survey of approaches to description and recognition of dynamic textures. To our best knowledge, no such survey is currently available. Our survey is limited to temporal textures: we do not consider the other two classes of motion patterns. Even within DT area, our attention is further limited to characterisation and recognition only. In particular, we do not address DT modelling and synthesis, except for the case when model parameters are used for recognition. (For recent work on synthesis, see [16, 7, 8, 33].) Basically, we will deal with dynamic texture descriptors, or features, that have the potential of being used for DT detection, segmentation, recognition and indexing in video. When video is not segmented, that is, when the exact spatiotemporal extent of a DT is unknown, the features should combine computational efficiency with robustness and descriptive power. In addition, when spatial orientation and scale are also unknown, the features have to be scale- and orientationinvariant, at least to a certain extent. For this reason, in our survey we will pay attention to spatiotemporal directionality (anisotropy), periodicity and scale as the basic structural features which are closely related to the desired properties.
2 Characterisation and recognition of temporal texture Before discussing the existing approaches to dynamic texture, let us dwell on the tasks of DT analysis. These tasks are similar to those of the conventional spatial texture analysis: detection, segmentation, recognition, and indexing for retrieval. However, working with videos containing temporal textures of unknown spatiotemporal extent is different from working with static images. The difference is not just an additional dimension; it rather relates to the greater ‘fuzziness‘ of dynamic textures. Firstly, sequences showing physical processes like fire or smoke are difficult to segment: the visible spatial extent of such DT is permanently varying and less distinct. A dynamic texture (for example, smoke) can be partially transparent, so one may face the problem of motion-based separation of a DT from textured background. Problems like that are not addressed in traditional texture analysis. Secondly, in temporal textures we categorise both motion pattern and appearance. One may be interested in any flag flapping in the wind, or in flag of a certain country flapping in the wind. The categories are more general and more fuzzy, like ‘gentle sea waves’ or ’rough turbulent water’. In static texture recognition, the classes are usually more strict and welldefined. However, the perceptual grouping of static textures may also occur at different levels. For example, one may speak of ‘wood texture’ in general, or of the texture of a certain type of wood. The well-known experiment by
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Rao and Lohse [25] demonstrated that, in absence of any prior information and specific task to solve, the process of perceptual texture grouping in humans is driven by fundamental structural features such as directionality versus non-directionality, periodicity versus irregularity and, probably, structural complexity. (The latter is closely related to the level of detail, or scale.) When people are asked to repeatedly group texture patterns into more general categories by merging some of the previously obtained groups, the fundamental features play a dominant role. Their relevance to static texture analysis has been proved by numerous studies. It is reasonable to assume that they will be important in dynamic texture analysis as well, especially when a high degree of invariance is desired. The existing approaches to temporal texture recognition can be classified into one of the following groups: methods based on optic flow, methods computing geometric properties in the spatiotemporal domain, methods based on local spatiotemporal filtering, methods using global spatiotemporal transforms and, finally, model-based methods that use estimated model parameters as features. Methods based on optic flow [18, 24, 1, 9, 10, 20, 21, 17, 22, 23] are currently the most popular because optic flow estimation is a computationally efficient and natural way to characterise the local dynamics of a temporal texture. It helps reduce dynamic texture analysis to analysis of a sequence of instantaneous motion patterns viewed as static textures. When necessary, image texture features can be added to the motion features, to form a complete feature set for motion- and appearance-based recognition. It is well-known [15, 30] that on a smooth moving contour one can locally determine only the velocity component normal to the contour; the tangential component cannot be obtained. (This is called the aperture problem.) In the case of optic flow, the normal flow can only be assigned to a pixel unless a larger region is considered and additional smoothness constraints are introduced. The normal flow is orthogonal to the contour and (anti-) parallel to the spatial image gradient. Its computation only needs the three partial derivatives of the spatiotemporal image function. Obtaining the complete flow vector requires more effort, and care should be taken not to enforce smoothness across motion discontinuities. Advantages and drawbacks of the two types of flow are discussed in section 3 in more detail. In the early studies by Nelson and Polana [18, 24], the vector field of the normal flow was used to form features characterising the overall magnitude and directionality of motion, as well as local image deformations due to motion. Spatial co-occurrence matrices for normal flow directions within pixel neighbourhood were also considered to obtain directional difference statistics. The directionality was evaluated by accumulating a coarse histogram of flow directions and computing the absolute deviation from a uniform distribution. Local image deformations were described by the divergence and the curl of the normal flow field. The features were tested in a classification experiment [24] with seven motion sequences, including five DTs.
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It is interesting that the early studies [18, 24] pay proper attention to the issue of invariance, while most of the later studies do not do that. To obtain a temporal and spatial scale-invariant measure, the average flow magnitude is scaled by its standard deviation, resulting in the ‘peakiness’ feature used later in [22, 23]. The directional difference features are also defined and normalised so as to provide invariance under translation, rotation and scaling in the image plane. The divergence and the curl are scale-dependent, but their ratio can be used to obtain an invariant feature. The influence of the Nelson and Polana’s work can be traced in more recent studies using optic flow to define DT features. In particular, this concerns the assumption that the normal flow is sufficient for adequate description of temporal texture dynamics, shared by most of the authors [1, 9, 10, 20, 21, 22, 23]. Fablet and Bouthemy published a series of studies [1, 9, 10] devoted to recognition of dynamic texture and other motion patterns. They introduced temporal co-occurrence that measures the probability of co-occurrence in the same image location of two normal velocities (normal flow magnitudes) separated by a certain temporal interval. In the early paper [1], three fixed intervals (1,4, or 8 frames) are considered and standard co-occurrence features [13] are used to discriminate between four different motion sequences, including one temporal texture. Later on, the authors developed a more sophisticated approach [10] that accounts for both temporal and spatial aspects of image motion. The method captures the co-occurrence statistics using temporal multiscale Gibbs models. The temporal co-occurrence is defined for consecutive frames, the spatial co-occurrence for neighbouring scales. The maximum likelihood model is obtained for each class in the learning stage. The ML criterion is also used to classify a motion sequence in the classification stage. Eight classes are considered in the tests, including five DT categories: wind-blown trees and grass, gentle waves, turbulent flows, moving escalators. Each sequence is cropped to ‘pure’ dynamic texture. The methods [1, 10] are limited in their capability to capture spatial and temporal periodicity of dynamic textures. The initial method [1] does not describe directionality at all; in the enhanced method [10], some information related to anisotropy may be hidden in the estimated model parameters. Although the issue of invariance is not addressed, both methods are probably rotation-invariant. Despite its sophistication, the enhanced method does not seem applicable to large sets of temporal textures or to non-segmented videos. Peh and Cheong’s work [20, 21] builds on that of Nelson and Polana [18] and Bouthemy and Fablet [1]. In [21], the magnitude and the direction of the normal flow are quantised into a small number of levels. Then two spatiotemporal maps are built that trace motion history through a number of previous frames. In each currently moving pixel, the magnitude map is set to the current flow magnitude. If the pixel has been stationary for τ previous frames, the map is set to zero. Otherwise, the map is set to the magnitude the flow had τ frames ago. A similar map is accumulated for the normal flow direction
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as well. In the experimental study, a fixed value τ = 5 is used and 10 DT classes are considered. Each sequence is divided into subsequences of τ = 5 frames each, used as samples. The magnitude and the direction maps of each sample are treated as image textures, and six conventional texture features are selected for classification tests. The classification results are compared to those achieved on the same data by the methods of Nelson and Polana [18] and Bouthemy and Fablet [1]. For most classes the reported success rates by the proposed method are higher than by the other two methods, with [1] being the least successful. The approach [20, 21] is simple, fast and rotation-invariant up the to quantisation error in the flow direction. The features convey directional information; however, temporal periodicity analysis with fixed τ is impossible. Recently, P´eteri and Chetverikov [22, 23] have proposed a method that combines normal flow features with periodicity features, in an attempt to explicitely characterise both motion magnitude, directionality and periodicity. The normal flow features used are similar to [18] (peakiness, divergence, curl); however, a novel feature of orientation homogeneity of the normal flow field was also introduced. In addition, two spatiotemporal periodicity features were proposed based on the maximal regularity MR , which is a measure of spatial periodicity of an image texture [4]. When applied to a dynamic texture, the method evaluates the temporal variation of spatial periodicity. For each frame t of a DT, MR is computed in a sliding window. Then the largest value is selected, corresponding to the most periodic patch within the frame. This provides a largest periodicity value, P (t), for each t. The mean and the variance of P (t) are currently used as DT features. Some initial DT classification results are reported in [23]. The approach [22, 23] is rotation-invariant. Its periodicity-related part is affine-invariant. (See [4] for details.) Although promising, the temporal regularity method should be improved in at least two aspects: it should only be applied to the moving part of the image, for example, to thresholded optic flow; and the periodicity of P (t) should be analysed. The last optical flow-based approach we are going to mention is presented in the recent paper by Lu and co-authors [17]. This study is unique in that it uses complete not normal flow vectors. In addition, acceleration vectors are also computed. The 3D structure tensor technique (with spatiotemporal gradient) is used to obtain the complete flow vector by minimising an energy function in a neighbourhood of a pixel. To reduce the effect of the aperture problem, the eigenvectors of the tensor are calculated and combined into a measure of spatial ‘cornerity’ of the pixel. This measure is used as the weight in the histograms of velocity and acceleration: the higher the confidence of velocity estimation the larger the weight. These histograms are used to calculate the distance matrix for 7 DT sequences. To account for scale, a spatiotemporal Gaussian filter is applied to decompose a sequence into two spatial and two temporal resolution levels. The method [17] is rotation-invariant and it
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provides local directionality information; however, no higher-level structural analysis (e.g., periodicity evaluation) is possible. The other four groups of methods are much less popular than the methods based on the optic flow. Methods computing geometric properties in the spatiotemporal domain are represented by two studies: the initial algorithm by Otsuka and co-authors [19] and its modification by Zhong and Sclaroff [34]. Otsuka and co-authors [19] assume that DTs can be represented by moving contours whose motion trajectories can be tracked. They consider trajectory surfaces within 3D spatiotemporal volume data and extract temporal and spatial features based on the tangent plane distribution. The latter is obtained using 3D Hough transform. Two groups of features, spatial and temporal, are then calculated. The spatial features include the directionality of contour arrangement and the scattering of contour placement. The temporal features characterise the uniformity of velocity components, the flash motion ratio and the occlusion ratio. The features were used to classify four DTs. It is well-known that motion velocity is closely related to geometry in the spatiotemporal domain; it is also known that considering trajectories in this domain may help resolve ambiguities due to temporary occlusion [15]. However, for dynamic textures the assumption of good trajectory surfaces being available is not realistic. Zhong and Sclaroff [34] tried to avoid the problem by using 3D edges in the spatiotemporal domain. Their DT features are computed for voxels taking into account the spatiotemporal gradient. Unfortunately, the method and the results are not convincing enough, and research in this direction currently seems to have no continuation. Methods based on local spatiotemporal filtering are represented by a single study by Wildes and Bergen [31] mentioned here for completeness. The analysis of local spatiotemporal pattern, its orientation and energy, is useful for qualitative classification of local motion structure into such categories as stationary, coherent, incoherent, flickering and scintillating. Results in [31] show correlation between the qualitative features and the character of motion, assuming that small and short DTs are considered. However, motion in different parts of a dynamic texture can be different. No method is given how to combine the local qualitative values into a global description, or how to characterise fundamental structural properties of entire DT. Recently, there have been attempts [28] of video texture indexing using spatiotemporal wavelets. The emerging use of global spatiotemporal transforms indicates the necessity to characterise motion at different spatiotemporal scales. Spatiotemporal wavelets can decompose motion into local and global, according to the desired degree of detail. For example, a tree waving in the wind shows a coarse motion of trunk, a finer motion of branches and still finer motion of leaves. The periodicities of these motions are also different, resulting in energy maxima at different scales. These effects can hopefully be captured by spatiotemporal wavelets. Another argument in favour of wavelets is the fact that the MPEG-7 multimedia standard proposes the use of Gabor
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wavelet features for image texture browsing and retrieval [32]. An argument against global spatiotemporal transforms is the difficulty to provide rotation invariance. Finally, let us briefly discuss studies devoted to model-based DT recognition. Impressive results have recently been achieved in DT synthesis using the framework based on a system identification theory which estimates the parameters of a stable dynamic model [16, 8, 33]. Saisan and co-authors [26] applied the dynamic texture model [7] to recognition of 50 different temporal textures. Despite this success, the applicability of the approach to real videos is doubtful for several reasons: it is time-consuming; it assumes stationary DTs well-segmented in space and time, and the accuracy drops drastically if they are not; it is difficult to define a metric in the space of dynamic models. Fujita and Nayar [11] modified the approach [26] by using impulse responses of state variables to identify model and texture. Their approach is applicable to multiple dynamic textures in different regions of the image, is faster than [26] and shows less sensitivity to non-stationarity. However, the problem of heavy computational load and the issues of scalability and invariance remain open.
3 Discussion and conclusion Dynamic texture recognition is a new area whose history dates back to less than 15 years ago. It is natural that many of the proposed methods build on the experience gained in static texture analysis and try to combine optic flow with multiresolution histograms, co-occurrence and other known tools. And, probably, it is too early to make general conclusions about the development of the area. At the same time, we can already learn from the past and summarise some major issues that should be addressed in the future. The first issue is the normal flow vector versus the complete flow vector. Optic flow is the basis for most of the current methods, and both versions have their advantages and drawbacks. As already mentioned, normal flow is purely local, fast to compute and does not tend to extend motion over discontinuities; it has been demonstrated to contain usable motion information. On the other hand, normal flow, even in its regularised form (smoothing, thresholding), is noise-sensitive. Its close relation to the spatial gradient, that is, to contours and shapes, implies that normal flow features correlate with appearance features. This was acknowledged in [24] as a negative aspect, but no real solution was proposed. Fablet and Bouthemy [10] even claim that the direction of normal flow contains no independent motion information; they only use magnitude. The applicability of normal flow to rotational motion (like the ‘toilet’ sequence of Szummer [29]) is questionable. In general, the examples of normal flow fields given in the literature do not reflect well the visual dynamics of the processes. The regularised complete flow field is much better in that respect. However, the iterative schemes for complete flow need more computation and tend to extend motion over discontinuities. (The latter
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is usually harmful, but sometimes it can be useful for overcoming short occlusions.) Both problems have been addressed in the recent research on optic flow estimation. Using modern multigrid numerical schemes, one can achieve near real-time performance on a general-purpose computer (27 fps for 200 × 200 size frames [3].) Motion borders can be preserved by using the total variation of the flow field (with the 3D gradient) as the smoothing term [2]. We plan to compare the two types of flow on a large database of dynamic textures. The problem of combining motion features with appearance features is also open. As already mentioned, it is task-dependent. In some cases, we are only interested in the motion pattern; in other cases, we are interested in both motion and appearance. Imagine we search in videos for any flag waving in the wind. If normal flow strongly depends on appearance, learning on flag of a certain country (that is, with a specific picture) would not be wise. A major open issue is that of capturing temporal periodicity. Many of DTs are quasi-periodic, and sometimes we humans recognise them due to this property. However, neither of the existing approaches treats the temporal periodicity properly. The reason is that recognition of periodicity requires correlating frames separated by an unknown and, possibly, large interval. This is computationally expensive, while for video processing one normally needs fast methods. Here, spatiotemporal multiscale (multiresolution) approaches may prove useful. The question of invariance, both geometric and photometric, arises each time the viewing conditions are not constrained. When videos of outdoor temporal textures are taken, this is often the case. Ideally, we should be prepared to cope with perspective distortion, or, at least, with affine image distortion corresponding to the weak perspective model [14]. Currently, rotation and scaling in the image plane is the maximum we can handle. In that respect, it will be very useful to learn from the related recent efforts in 3D computer vision, such as [27]. Finally, much more attention should be paid to creating test data and designing experimental protocols for proper evaluation and comparison of the emerging techniques. What we typically have now is comparison on a few (maximum 10) randomly selected dynamic patterns, often from the obsolete and poor-quality Szummer dataset [29]. Classification experiments with such limited data are of limited significance: one could probably obtain similar accuracy by considering single frames instead of sequences, as the image textures involved are distinct enough. Also, we have to clarify if the DTs considered are pre-segmented or not. For example, Saisan and co-authors [26] use a large set of 50 pre-segmented DTs and report a good overall classification accuracy of almost 90%. When applied to a small set of only 5 DTs, but with 2 unsegmented patterns [11], the method yields the average of 58% because of complete failure in these two unsegmented cases. To meet the need for a comprehensive database of dynamic textures, in the framework of the European FP6 Network of Excellence MUSCLE we are now creating a large dataset that will be available on the web site of the
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NoE: http://www.muscle-noe.org/. This survey was also supported by the MUSCLE NoE. We finish our survey and discussion by concluding that dynamic texture recognition is a novel, exciting and developing research area, where some progress has already been achieved, but a lot of work is still to be done.
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