Unsupervised Learning Consensus Model for Dynamic Texture Videos Segmentation
This work addresses the lack of annotated data for dynamic texture segmentation, offering an unsupervised solution that is incremental in improving efficiency and simplicity over deep learning approaches.
The paper tackles dynamic texture video segmentation without requiring annotated training data by proposing an unsupervised learning consensus model (ULCM) that merges multiple weak segmentation maps, achieving competitive performance on the YUP++ dataset and being significantly faster and simpler than existing methods.
Dynamic texture (DT) segmentation, and video processing in general, is currently widely dominated by methods based on deep neural networks that require the deployment of a large number of layers. Although this parametric approach has shown superior performances for the dynamic texture segmentation, all current deep learning methods suffer from a significant main weakness related to the lack of a sufficient reference annotation to train models and to make them functional. This study explores the unsupervised segmentation approach that can be used in the absence of training data to segment new videos. We present an effective unsupervised learning consensus model for the segmentation of dynamic texture (ULCM). This model is designed to merge different segmentation maps that contain multiple and weak quality regions in order to achieve a more accurate final result of segmentation. The diverse labeling fields required for the combination process are obtained by a simplified grouping scheme applied to an input video (on the basis of a three orthogonal planes: xy, yt and xt). In the proposed model, the set of values of the requantized local binary patterns (LBP) histogram around the pixel to be classified are used as features which represent both the spatial and temporal information replicated in the video. Experiments conducted on the challenging SynthDB dataset show that, contrary to current dynamic texture segmentation approaches that either require parameter estimation or a training step, ULCM is significantly faster, easier to code, simple and has limited parameters. Further qualitative experiments based on the YUP++ dataset prove the efficiently and competitively of the ULCM.