I. INTRODUCTION
 Sparse representation-based classification (SRC) has
recently attracted substantial research attention
...
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Sub-sampled dictionaries for coarse-to-fine sparse representation-based human action recognition

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Sub-Sampled Dictionaries for Coarse-to-Fine Sparse Representation-based Human Action Recognition. Poster presented at the main track of ICME 2014.

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Sub-sampled dictionaries for coarse-to-fine sparse representation-based human action recognition

  1. 1. I. INTRODUCTION  Sparse representation-based classification (SRC) has recently attracted substantial research attention  However, the computational complexity of testing makes it challenging to deploy SRC in practice  We propose a novel method for human action recognition, leveraging coarse-to-fine sparse representations that have been obtained through dictionary sub-sampling  The proposed method reduces the time complexity of testing at no substantial loss in recognition accuracy JongHo Leea, Hyun-seok Mina, Jeong-jik Seoa, Wesley De Nevea,b, and Yong Man Roa aImage and Video Systems Lab, KAIST, Republic of Korea bMultimedia Lab, Ghent University-iMinds, Belgium website: http://ivylab.kaist.ac.kr IEEE International Conference on Multimedia & Expo (ICME), July 2014, Chengdu, China SUB-SAMPLED DICTIONARIES FOR COARSE-TO-FINE SPARSE REPRESENTATION-BASED HUMAN ACTION RECOGNITION e-mail: ymro@ee.kaist.ac.kr II. PROPOSED APPROACH 1. Training Fig. 2. Time complexity of different human action recognition approaches. Fig. 1. Accuracy of different human action recognition approaches. 0 10 20 30 40 50 60 70 150 300 450 600 750 900 1050 1200 1350 1500 Timecomplexity(s) Number of atoms(ls) Proposed method with ds =48 Proposed method with ds =72 Proposed method with ds =144 Conventional method 0.76 0.78 0.8 0.82 0.84 0.86 0.88 0.9 150 300 450 600 750 900 1050 1200 1350 1500 Recognitionaccuracy Number of atoms(ls) Proposed method with ds =48 Proposed method with ds =72 Proposed method with ds =144 Conventional method III. EXPERIMENTS 1. Experimental setup  Dataset: UCF-50  Feature: Cuboid detector + HOG descriptor  Homotopy-based 𝑙1-norm minimization 2. Experimental results  Conventional method: classification only uses the FGD IV. CONCLUSIONS  We proposed a novel method for human action recognition using coarse-to-fine sparse representations  The proposed method achieves efficient human action recognition at no substantial loss in recognition accuracy 2. Testing Y Y𝑠 Random projection Feature Extraction Test video clip … Class 1 Class 2 Φ 𝑠,1 Φ 𝑠,2 Φ 𝑠,3 Φ 𝑠,𝐾 Sparse Coefficients Y𝑠 Ranking 1 𝐻+1 𝐻+4 𝑯 Candidate Actions Candidate Action Selection Coarse-Grained Dictionary (CGD) O X X O We select 𝐻 candidate actions Feature Extraction … Action 1 Action 2 Action 3 Action 𝐾 Training Dataset Action 1 Action 2 Action 3 Action 𝐾 … … … … … Action 1Action 2Action 3 Action 𝐾 Fine-Grained Dictionary (FGD) Coarse-Grained Dictionary (CGD)Φ 𝑠,1 Φ 𝑠,2 Φ 𝑠,3 Φ 𝑠,𝐾 Φ 𝑜,1 Φ 𝑜,2 Φ 𝑜,3 Φ 𝑜,𝐾 Random projection (for reducing the dimension of the atoms) Random sampling (for reducing the number of atoms) Dictionary Construction Action 1 Action 2 Action 3 Action 𝐾 … … Pruned FGD Φ 𝑜,1 Φ 𝑜,2 Φ 𝑜,3 Φ 𝑜,𝐾 Action 1 Action 2 Action 3 Action 𝐾 … Candidate Actions O X X O Φ 𝑝𝑟,1 Action 1 Φ 𝑝𝑟,𝐻 Action 𝐾 … Pruned FGD𝐃 𝑝𝑟  Classification  We can find the sparse representation 𝐗 𝑝𝑟 of 𝐘 with 𝐃 𝑝𝑟 𝐘 = 𝐲1, 𝐲2, … , 𝐲 𝑚 , 𝐗 𝑝𝑟 = [𝐱 𝑝𝑟,1, 𝐱 𝑝𝑟,2, … , 𝐱 𝑝𝑟,𝑚]  We label 𝐕 with the action 𝑘 that comes with the smallest residual error 𝒓 𝑘 𝐲 𝒓 𝑘 𝐘 = 1 𝑚 𝑖=1 𝑚 𝐲𝑖 − 𝐃 𝑝𝑟 𝜹 𝑘 𝐱 𝑝𝑟,𝑖 𝟏  𝜹 𝒌 𝐱 𝑝𝑟,𝑖 is a new vector whose only nonzero entries are the entries in 𝐱 𝑝𝑟,𝑖 associated with the action 𝑘 Φ 𝑜,1 Φ 𝑜,2 Φ 𝑜,3 Φ 𝑜,𝐾

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