Dynamic textures exist in various forms, e.g., fire, smoke, and traffic jams, but recognizing dynamic texture is challenging due to the complex temporal variations. In this paper, we present a novel approach stemmed from slow feature analysis (SFA) for dynamic texture recognition. SFA extracts slowly varying features from fast varying signals. Fortunately, SFA is capable to leach invariant representations from dynamic textures. However, complex temporal variations require high-level semantic representations to fully achieve temporal slowness, and thus it is impractical to learn a high-level representation from dynamic textures directly by SFA. In order to learn a robust low-level feature to resolve the complexity of dynamic textures, we propose manifold regularized SFA (MR-SFA) by exploring the neighbor relationship of the initial state of each temporal transition and retaining the locality of their variations. Therefore, the learned features are not only slowly varying, but also partly predictable. MR-SFA for dynamic texture recognition is proposed in the following steps: 1) learning feature extraction functions as convolution filters by MR-SFA, 2) extracting local features by convolution and pooling, and 3) employing Fisher vectors to form a video-level representation for classification. Experimental results on dynamic texture and dynamic scene recognition datasets validate the effectiveness of the proposed approach.
Local features have been successfully applied to dynamic texture recognition. Local binary patterns on three orthogonal planes (LBP-TOP) were proposed for dynamic texture and facial expression recognition @cite_42 . Instead of processing the entire video, this approach extracts features from three orthogonal planes in the video cube. LBP-TOP has been generalized to the tensor orthogonal LBP for micro-expression recognition @cite_3 . Similar to LBP-TOP, the method of multiscale binarized statistical image features on three orthogonal planes (MBSIF-TOP) was proposed using binarized responses of filters learned by applying independent component analysis on each plane @cite_40 . By capturing the direction of natural flows, a spatiotemporal directional number transitional graph (DNG) was proposed using spatial structures and motions of each local region @cite_10 . Although these approaches work well, they neglect a large amount of spatial-temporal information.
@cite_40 @cite_42 @cite_10 @cite_3
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Local binary pattern from three orthogonal planes (LBP-TOP) has been widely used in emotion recognition in the wild. However, it suffers from illumination and pose changes. This paper mainly focuses on the robustness of LBP-TOP to unconstrained environment. Recent proposed method, spatiotemporal local monogenic binary pattern (STLMBP), was verified to work promisingly in different illumination conditions. Thus this paper proposes an improved spatiotemporal feature descriptor based on STLMBP. The improved descriptor uses not only magnitude and orientation, but also the phase information, which provide complementary information. In detail, the magnitude, orientation and phase images are obtained by using an effective monogenic filter, and multiple feature vectors are finally fused by multiple kernel learning. STLMBP and the proposed method are evaluated in the Acted Facial Expression in the Wild as part of the 2014 Emotion Recognition in the Wild Challenge. They achieve competitive results, with an accuracy gain of 6.35 and 7.65 above the challenge baseline (LBP-TOP) over video. Micro-expressions are difficult to be observed by human beings due to its low intensity and short duration. Recently, several works have been developed to resolve the problems of micro-expression recognition caused by subtle intensity and short duration. One of them, Local binary pattern from three orthogonal planes (LBP-TOP) is primarily used to recognize micro-expression from the video recorded by high-speed camera. Several variances of LBP-TOP have also been developed to promisingly improve the performance of LBP-TOP for microexpression recognition. However, these variances of LBP-TOP including LBP-TOP cannot well extract the subtle movements of micro-expression so that they have the low performance. In this paper, we propose spontaneous local radon-based binary pattern to analyze micro-expressions with subtle facial movements. Firstly, it extracts the sparse information by using robust principal component analysis since micro-expression data are sparse in both temporal and spatial domains caused by short duration and low intensity. These sparse information can provide much motion information to dynamic feature descriptor. Furthermore, it employs radon transform to obtain the shape features from three orthogonal planes, as radon transform is robustness to the same histogram distribution of two images. Finally, one-dimensional LBP is employed in these shape features for constructing the spatiotemporal features for microexpression video. Intensive experiments are conducted on two available published micro-expression databases including SMIC and CASME2 databases for evaluating the performance of the proposed method. Experimental results demonstrate that the proposed method achieves promising performance in microexpression recognition. Spontaneous facial micro-expression analysis has become an active task for recognizing suppressed and involuntary facial expressions shown on the face of humans. Recently, Local Binary Pattern from Three Orthogonal Planes (LBP-TOP) has been employed for micro-expression analysis. However, LBP-TOP suffers from two critical problems, causing a decrease in the performance of micro-expression analysis. It generally extracts appearance and motion features from the sign-based difference between two pixels but not yet considers other useful information. As well, LBP-TOP commonly uses classical pattern types which may be not optimal for local structure in some applications. This paper proposes SpatioTemporal Completed Local Quantization Patterns (STCLQP) for facial micro-expression analysis. Firstly, STCLQP extracts three interesting information containing sign, magnitude and orientation components. Secondly, an efficient vector quantization and codebook selection are developed for each component in appearance and temporal domains to learn compact and discriminative codebooks for generalizing classical pattern types. Finally, based on discriminative codebooks, spatiotemporal features of sign, magnitude and orientation components are extracted and fused. Experiments are conducted on three publicly available facial micro-expression databases. Some interesting findings about the neighboring patterns and the component analysis are concluded. Comparing with the state of the art, experimental results demonstrate that STCLQP achieves a substantial improvement for analyzing facial micro-expressions. HighlightsWe propose spatiotemporal completed local quantized pattern for micro-expression analysis.We propose to use three useful information, including the sign-based, magnitude-based and orientation-based difference of pixels for LBP.We propose to use an efficient vector quantization and discriminative codebook selection to make LBP-TOP more discriminative and compact.We evaluate the framework on three publicly available facial micro-expression databases.We evaluate the influence of parameters, different components and codebook selection to spatiotemporal completed local quantized pattern. Local binary patterns of more bits extracted in a large structure have shown promising results in visual recognition applications. This results in very high-dimensional data so that it is not feasible to directly extract features from the LBP histogram, especially for a large-scale database. Instead of extracting features from the LBP histogram, we propose a new approach to learn discriminative LBP structures for a specific application. Our objective is to select an optimal subset of binarized-pixel-difference features to compose the LBP structure. As these features are strongly correlated, conventional feature-selection methods may not yield a desirable performance. Thus, we propose an incremental Maximal-Conditional-Mutual-Information scheme for LBP structure learning. The proposed approach has demonstrated a superior performance over the state-of-the-arts results on classifying both spatial patterns such as texture classification, scene recognition and face recognition, and spatial-temporal patterns such as dynamic texture recognition. HighlightsWe propose a new approach to tackle high-dimensional LBP features.It discovers optimal LBP structure to generate discriminative features.We propose a MCMI scheme for LBP structure learning to handle pixel correlation.It demonstrates a superior performance to SOTA on various visual applications.