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Graph Regularization for Cuboid-Based Video Analysis

This progress report discusses the implementation of a graph regularization approach for cuboid-based video processing, achieving 97.47% sparsity and 0.6667% reconstruction error. The previous outlier rate of 31.86% has significantly decreased to 1.65%. By dividing image volumes into cuboids, learning dictionaries, and applying graph regularization, the method successfully clusters temporal blocks of cuboids for video analysis and visualization.

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Graph Regularization for Cuboid-Based Video Analysis

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  1. Progress Report: Week 6 Alvaro Velasquez

  2. Recap • Overlapping, overcomplete dictionary. • Heat kernel affinity matrix. • Graph Regularization term. • Max Voting scheme. • Sparsity: 97.47% • Reconstruction error: 0.6667% • Previous framework outlier rate: 31.86% • Current framework outlier rate: 1.65%

  3. Previous Framework: Lasso

  4. Current framework: Graph Regularization

  5. Current Work • Divide image volume into cuboids and vectorize them. • Learn a dictionary of cuboids. • Calculate sparse coefficient matrix of temporal blocks of cuboids using graph regularization. • Cluster vectors of the sparse coefficient matrix using k-means. • Display cluster centers on video.

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