Sub-sampled dictionaries for coarse-to-fine sparse representation-based human action recognition
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The document presents a novel method for human action recognition using coarse-to-fine sparse representations to reduce computational complexity. It highlights the efficiency of this approach compared to conventional sparse representation-based classification while maintaining accuracy. Experimental results demonstrate the proposed method's effectiveness in recognizing actions with minimal time complexity.
Sub-sampled dictionaries for coarse-to-fine sparse representation-based human action recognition
- 1. Poster Spotlights SUB-SAMPLED DICTIONARIES FOR COARSE-TO-FINE SPARSE REPRESENTATION-BASED HUMAN ACTION RECOGNITION Poster Session 5, July 17h JongHo Lee, Hyun-seok Min, Jeong-jik Seo, Wesley De Neve, and Yong Man Ro 506
- 2. SUB-SAMPLED DICTIONARIES FOR COARSE-TO-FINE SPARSE REPRESENTATION-BASED HUMAN ACTION RECOGNITION 1. Introduction Sparse representation-based classification (SRC) has recently attracted much attention However, the computational complexity of SRC makes its usage challenging in practice We propose a novel method for human action recognition, leveraging coarse-to-fine sparse representations 2. Proposed Method The time complexity of SRC depends on the dictionary size 4. Conclusions We proposed a novel method for human action recognition using coarse-to-fine sparse representations This proposed method is able to achieve efficient human action recognition with no substantial loss in accuracy 3. Experimental Results A: Conventional SRC (using only the Fine-Grained Dictionary) B: Proposed Method (using Coarse-to-Fine Representations) 1 2 3 4 K… 1 2 3 4 K… 1 4 H… Coarse-Grained Dictionary Fine-Grained Dictionary Pruned Fine-Grained Dictionary Candidate Classes 67.5 32.8 0.0 20.0 40.0 60.0 80.0 A B Timecomplexity Time Complexity (s) 0.8438 0.8567 0.835 0.84 0.845 0.85 0.855 0.86 A B RecognitionAccuracy Accuracy