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• W3208781631 abstract "Representation of dynamic textures (DTs), well-known as a sequence of moving textures, is a challenge in video analysis for various computer vision applications. It is partly due to disorientation of motions, the negative impacts of the well-known issues on capturing turbulent features: noise, changes of environment, illumination, similarity transformations, etc. In this work, we introduce significant solutions in order to deal with above problems. Accordingly, three streams of those are proposed for encoding DTs: i) based on dense trajectories extracted from a given video; ii) based on robust responses extracted by moment models; iii) based on filtered outcomes which are computed by variants of Gaussian-filtering kernels. In parallel, we also propose several discriminative descriptors to capture spatio-temporal features for above DT encodings. For DT representation based on dense trajectories, we firstly extract dense trajectories from a given video. Motion points along the paths of dense trajectories are then encoded by our xLVP operator, an important extension of Local Vector Patterns (LVP) in a completed encoding context, in order to capture directional dense-trajectory-based features for DT representation.For DT description based on moment models, motivated by the moment-image model, we propose a novel model of moment volumes based on statistical information of spherical supporting regions centered at a voxel. Two these models are then taken into account video analysis to point out moment-based images/volumes. In order to encode the moment-based images, we address CLSP operator, a variant of completed local binary patterns (CLBP). In the meanwhile, our xLDP, an important extension of Local Derivative Patterns (LDP) in a completed encoding context, is introduced to capture spatio-temporal features of the moment-volume-based outcomes. For DT representation based on the Gaussian-based filterings, we will investigate many kinds of filterings as pre-processing analysis of a video to point out its filtered outcomes. After that, these outputs are encoded by discriminative operators to structure DT descriptors correspondingly. More concretely, we exploit the Gaussian-based kernel and variants of high-order Gaussian gradients for the filtering analysis. Particularly, we introduce a novel filtering kernel (DoDG) in consideration of the difference of Gaussian gradients, which allows to point out robust DoDG-filtered components to construct prominent DoDG-based descriptors in small dimension. In parallel to the Gaussian-based filterings, some novel operators will be introduced to meet different contexts of the local DT encoding: CAIP, an adaptation of CLBP to fix the close-to-zero problem caused by separately bipolar features; LRP, based on a concept of a square cube of local neighbors sampled at a center voxel; CHILOP, a generalized formulation of CLBP to adequately investigate local relationships of hierarchical supporting regions. Experiments for DT recognition have validated that our proposals significantly perform in comparison with state of the art. Some of which have performance being very close to deep-learning approaches, expected as one of appreciated solutions for mobile applications due to their simplicity in computation and their DT descriptors in a small number of bins." @default.
• W3208781631 created "2021-11-08" @default.
• W3208781631 creator A5070408153 @default.
• W3208781631 date "2020-11-27" @default.
• W3208781631 modified "2023-09-23" @default.
• W3208781631 title "Efficient Representations of Dynamic Textures" @default.
• W3208781631 hasPublicationYear "2020" @default.
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