Learning Hierarchical Spatio-temporal Features for Action Recognition with Independent Subspace Analysis
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Resource Overview
Professor Andrew N.G from Stanford University's Computer Science Department presented this paper at CVPR 2011. This repository contains the useful implementation code for the proposed method, featuring independent subspace analysis algorithms for hierarchical feature learning.
Detailed Documentation
In the 2011 Conference on Computer Vision and Pattern Recognition (CVPR), Professor Andrew N.G from Stanford University's Computer Science Department published a paper titled "Learning Hierarchical Spatio-temporal Features for Action Recognition with Independent Subspace Analysis". The paper introduces a novel approach for action recognition by learning hierarchical spatio-temporal features through independent subspace analysis. The implementation code includes key algorithms for feature extraction, subspace decomposition, and multi-layer temporal modeling, providing researchers with practical tools to understand and apply this methodology effectively. This code implementation is particularly valuable for studying feature hierarchy construction and temporal pattern recognition in video analysis.
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