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.

We present a novel solution to the combinatorial optimization problem of discretizing a large-scale multidimensional space by exploiting the discriminative power of linear programming. Our work is based on a novel approach which is a fast (in the sense of linear) optimization algorithm for both discrete and continuous optimization problems. We demonstrate that the performance of our method is comparable to or better than that of a traditional optimization technique, and to that of a fully-optimized algorithm.

Optimal Convergence Rate for the GQ Lambek transform

Nearest Local Average Post-Processing for Online Linear Learning

An Integrated Representational Model for Semantic Segmentation and Background Subtraction

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  • A Comparative Study of Threshold Based Methods for Multiplicative Data Analysis

    Tighter Bounds for Discrete OptimizationWe present a novel solution to the combinatorial optimization problem of discretizing a large-scale multidimensional space by exploiting the discriminative power of linear programming. Our work is based on a novel approach which is a fast (in the sense of linear) optimization algorithm for both discrete and continuous optimization problems. We demonstrate that the performance of our method is comparable to or better than that of a traditional optimization technique, and to that of a fully-optimized algorithm.


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