An Integrated Representational Model for Semantic Segmentation and Background Subtraction


An Integrated Representational Model for Semantic Segmentation and Background Subtraction – Segregating human action sequences from multiple frames in video is a challenging task in computer vision. For some frames, one considers the motion, body position and other motion-related attributes, and other frames, one investigates whether two frames are the same. In this paper, we propose a new multilinear multi-frame visual clustering protocol: Multilinear Multilinear Multilinear (RMML). RMML can automatically detect and classify the relationships between multiple frames and their individual attributes. This is particularly important for multi-view classification in video. Our approach considers the relationship between features of various motion pairs and allows a multi-view clustering approach to be carried out. We evaluate RMML through experiments on two real-world applications: the video sequence summarization task and semantic segmentation task of visual object segmentation. Our approach achieves a state-of-the-art classification accuracy on both tasks.

In this paper, we propose a novel unsupervised model based on a multi-level Gaussian process model to compute the structure of the data generated by a neural network. Unlike the previous unsupervised methods, our model performs well even on very sparse data. Extensive experiments on several real real world datasets demonstrate that our model outperforms existing unsupervised methods in terms of the average precision of the predictions.

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An Integrated Representational Model for Semantic Segmentation and Background Subtraction

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  • Structural Correspondence Analysis for Semi-supervised Learning

    Scalable Bayesian Matrix Completion with Stochastic Optimization and Coordinate UpdatesIn this paper, we propose a novel unsupervised model based on a multi-level Gaussian process model to compute the structure of the data generated by a neural network. Unlike the previous unsupervised methods, our model performs well even on very sparse data. Extensive experiments on several real real world datasets demonstrate that our model outperforms existing unsupervised methods in terms of the average precision of the predictions.


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