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CGA: a new feature selection model for visual human action recognition

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Abstract

Recognition of human actions from visual contents is a budding field of computer vision and image understanding. The problem with such a recognition system is the huge dimensions of the feature vectors. Many of these features are irrelevant to the classification mechanism. For this reason, in this paper, we propose a novel feature selection (FS) model called cooperative genetic algorithm (CGA) to select some of the most important and discriminating features from the entire feature set to improve the classification accuracy as well as the time requirement of the activity recognition mechanism. In CGA, we have made an effort to embed the concepts of cooperative game theory in GA to create a both-way reinforcement mechanism to improve the solution of the FS model. The proposed FS model is tested on four benchmark video datasets named Weizmann, KTH, UCF11, HMDB51, and two sensor-based UCI HAR datasets. The experiments are conducted using four state-of-the-art feature descriptors, namely HOG, GLCM, SURF, and GIST. It is found that there is a significant improvement in the overall classification accuracy while considering very small fraction of the original feature vector.

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References

  1. Aslan MF, Durdu A, Sabanci K (2020) Human action recognition with bag of visual words using different machine learning methods and hyperparameter optimization. Neural Comput Appl 32:8585–8597

    Google Scholar 

  2. Sahoo SP, Ari S (2019) On an algorithm for human action recognition. Expert Syst Appl 115:524–534

    Google Scholar 

  3. Saggese A, Strisciuglio N, Vento M, Petkov N (2019) Learning skeleton representations for human action recognition. Pattern Recognit Lett 118:23–31

    Google Scholar 

  4. Zhang P, Lan C, Xing J et al (2019) View adaptive neural networks for high performance skeleton-based human action recognition. IEEE Trans Pattern Anal Mach Intell 41:1963–1978

    Google Scholar 

  5. Ahmed S, Ghosh KK, Singh PK et al (2020) Hybrid of harmony search algorithm and ring theory-based evolutionary algorithm for feature selection. IEEE Access 8:102629–102645

    Google Scholar 

  6. Ghosh KK, Singh PK, Hong J et al (2020) Binary social mimic optimization algorithm with X-shaped transfer function for feature selection. IEEE Access 8(1):97890–97906. https://doi.org/10.1109/ACCESS.2020.2996611

    Article  Google Scholar 

  7. Ghosh KK, Ahmed S, Singh PK et al (2020) Improved binary sailfish optimizer based on adaptive β-hill climbing for feature selection. IEEE Access 8(1):83548–83560. https://doi.org/10.1109/ACCESS.2020.2991543

    Article  Google Scholar 

  8. Chatterjee B, Bhattacharyya T, Ghosh KK et al (2020) Late Acceptance Hill Climbing Based Social Ski Driver Algorithm for Feature Selection. IEEE Access 8:75393–75408. https://doi.org/10.1109/ACCESS.2020.2988157

    Article  Google Scholar 

  9. Ghosh M, Guha R, Mondal R et al (2018) Feature selection using histogram-based multi-objective GA for handwritten Devanagari numeral recognition. In: Bhateja V, Coello Coello C, Satapathy S, Pattnaik P (eds) Intelligent engineering informatics. Advances in intelligent systems and computing, vol 695. Springer, Singapore, pp 471–479. https://doi.org/10.1007/978-981-10-7566-7_46

  10. Ghosh M, Adhikary S, Ghosh KK et al (2019) Genetic algorithm based cancerous gene identification from microarray data using ensemble of filter methods. Med Biol Eng Comput 57:159–176

    Google Scholar 

  11. Ghosh M, Malakar S, Bhowmik S et al (2019) Feature selection for handwritten word recognition using memetic algorithm. In: Mandal J, Dutta P, Mukhopadhyay S (eds) Advances in intelligent computing. Studies in computational intelligence, vol 687. Springer, Singapore. https://doi.org/10.1007/978-981-10-8974-9_6

  12. Malakar S, Ghosh M, Bhowmik S et al (2020) A GA based hierarchical feature selection approach for handwritten word recognition. Neural Comput Appl 32:2533–2552

    Google Scholar 

  13. Singh PK, Sarkar R, Das N (2018) Benchmark databases of handwritten Bangla - Roman and Devanagari—Roman mixed-script document images. Multimedia Tools Appl 77:8441–8473

    Google Scholar 

  14. Ghosh M, Kundu T, Ghosh D, Sarkar R (2019) Feature selection for facial emotion recognition using late hill-climbing based memetic algorithm. Multimed Tools Appl 78:25753–25779. https://doi.org/10.1007/s11042-019-07811-x

    Article  Google Scholar 

  15. Saha S, Ghosh M, Ghosh S et al (2020) Feature Selection for Facial Emotion Recognition Using Cosine Similarity-Based Harmony Search Algorithm. Appl Sci 10:2816

    Google Scholar 

  16. Liu H, Motoda H (2007) Computational methods of feature selection. CRC Press, Boca Raton

    MATH  Google Scholar 

  17. Mitra P, Murthy CA, Pal SK (2002) Unsupervised feature selection using feature similarity. IEEE Trans Pattern Anal Mach Intell 24:301–312

    Google Scholar 

  18. Shang W-Q, Qu Y-L, Huang H-K et al (2006) Fuzzy knn text classifier based on gini index. J Guang xi Norm Univ Nat Sci Ed 24:87–90

    MATH  Google Scholar 

  19. Guha R, Ghosh M, Chakrabarti A et al (2020) Introducing clustering based population in Binary Gravitational Search Algorithm for Feature Selection. Appl Soft Comput 93:106341. https://doi.org/10.1016/j.asoc.2020.106341

    Article  Google Scholar 

  20. Dorigo M, Birattari M (2011) Ant colony optimization. In: Sammut C, Webb GI (eds) Encyclopedia of machine learning. Springer, pp 37–40

  21. Eberhart R, Kennedy J (1995) A new optimizer using particle swarm theory. In: Micro Machine and Human Science, 1995. MHS’95., Proceedings of the Sixth International Symposium on. IEEE, pp 39–43

  22. Ghosh M, Guha R, Singh PK et al (2019) A histogram based fuzzy ensemble technique for feature selection. Evol Intell 12(4):713–724

    Google Scholar 

  23. Ghosh M, Begum S, Sarkar R et al (2019) Recursive memetic algorithm for gene selection in microarray data. Expert Syst Appl 116:172–185

    Google Scholar 

  24. Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. IEEE Comput Soc Conf Comput Vis Pattern Recognit 1:886–893

    Google Scholar 

  25. Haralick RM, Shanmugam K Its’Hak Dinstein (1973) Textural Features for Image Classification. IEEE Trans Syst Man Cybern SMC-3(6):610–621

  26. Bay H, Tuytelaars T, Van Gool L (2006) Surf: Speeded up robust features. In: European conference on computer vision. Springer, pp 404–417

  27. Oliva A, Torralba A (2001) Modeling the shape of the scene: a holistic representation of the spatial envelope. Int J Comput Vis 42:145–175. https://doi.org/10.1023/A:1011139631724

    Article  MATH  Google Scholar 

  28. Blank M, Gorelick L, Shechtman E, et al (2005) Actions as space-time shapes. In: Tenth IEEE International Conference on Computer Vision (ICCV’05) Volume 1. IEEE, pp 1395–1402

  29. Schuldt C, Laptev I, Caputo B (2004) Recognizing human actions: a local SVM approach. In: Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004. IEEE, pp 32–36

  30. Liu J, Luo J, Shah M (2009) Recognizing realistic actions from videos in the wild. In: IEEE conference on computer vision and pattern recognition. Citeseer, pp 1996–2003

  31. Kuehne H, Jhuang H, Garrote E, et al (2011) HMDB: a large video database for human motion recognition. In: 2011 International conference on computer vision. IEEE, pp 2556–2563

  32. Anguita D, Ghio A, Oneto L et al (2013) A public domain dataset for human activity recognition using smartphones. In: Esann

  33. Anguita D, Ghio A, Oneto L, et al (2012) Human activity recognition on smartphones using a multiclass hardware-friendly support vector machine. In: International workshop on ambient assisted living. Springer, pp 216–223

  34. Niebles JC, Fei-Fei L (2007) A hierarchical model of shape and appearance for human action classification. In: 2007 IEEE conference on computer vision and pattern recognition. IEEE, pp 1–8

  35. Scovanner P, Ali S, Shah M (2007) A 3-dimensional sift descriptor and its application to action recognition. In: Proceedings of the 15th ACM international conference on Multimedia. pp 357–360

  36. Niebles JC, Wang H, Fei-Fei L (2008) Unsupervised learning of human action categories using spatial-temporal words. Int J Comput Vis 79:299–318

    Google Scholar 

  37. Ikizler-Cinbis N, Sclaroff S (2010) Object, scene and actions: Combining multiple features for human action recognition. In: European conference on computer vision. Springer, pp 494–507

  38. Huang W, Wu QMJ (2010) Human action recognition based on self organizing map. In: 2010 IEEE international conference on acoustics, speech and signal processing. IEEE, pp 2130–2133

  39. Chakraborty B, Holte MB, Moeslund TB, et al (2011) A selective spatio-temporal interest point detector for human action recognition in complex scenes. 1776–1783

  40. Reddy KK, Cuntoor N, Perera A, Hoogs A (2012) Human action recognition in large-scale datasets using histogram of spatiotemporal gradients. In: 2012 IEEE ninth international conference on advanced video and signal-based surveillance. IEEE, pp 106–111

  41. Yan X, Luo Y (2012) Recognizing human actions using a new descriptor based on spatial–temporal interest points and weighted-output classifier. Neurocomputing 87:51–61

    Google Scholar 

  42. Yuan C, Li X, Hu W, et al (2013) 3D R transform on spatio-temporal interest points for action recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. pp 724–730

  43. Wang L, Qiao Y, Tang X (2013) Motionlets: Mid-level 3D parts for human motion recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 2674–2681

  44. Sadek S, Al-Hamadi A, Krell G, Michaelis B (2013) Affine-invariant feature extraction for activity recognition. ISRN Mach Vis. Article ID 215195. https://doi.org/10.1155/2013/215195

  45. Solmaz B, Assari SM, Shah M (2013) Classifying web videos using a global video descriptor. Mach Vis Appl 24:1473–1485

    Google Scholar 

  46. Wang H, Kläser A, Schmid C, Liu C-L (2013) Dense trajectories and motion boundary descriptors for action recognition. Int J Comput Vis 103:60–79

    MathSciNet  Google Scholar 

  47. Cai Z, Wang L, Peng X, Qiao Y (2014) Multi-view super vector for action recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 596–603

  48. Wu J, Hu D, Chen F (2014) Action recognition by hidden temporal models. Vis Comput 30:1395–1404. https://doi.org/10.1007/s00371-013-0899-9

    Article  Google Scholar 

  49. Zhou W, Zhang Z (2014) Human action recognition with multiple-instance Markov model. IEEE Trans Inf Forensics Secur 9:1581–1591

    Google Scholar 

  50. Seto S, Zhang W, Zhou Y (2015) Multivariate time series classification using dynamic time warping template selection for human activity recognition. In: 2015 IEEE symposium series on computational intelligence. IEEE, pp 1399–1406

  51. Chen CY, Grauman K (2017) Efficient activity detection in untrimmed video with max-subgraph search. IEEE Trans Pattern Anal Mach Intell 39(5):908–921. https://doi.org/10.1109/TPAMI.2016.2564404

    Article  Google Scholar 

  52. Kushwaha AKS, Srivastava RA (2020) Framework for human activity recognition using pose feature for video surveillance system. Int J Comput Appl 975:8887

    Google Scholar 

  53. Luvizon DC, Tabia H, Picard D (2017) Learning features combination for human action recognition from skeleton sequences. Pattern Recognit Lett 99:13–20

    Google Scholar 

  54. Sharif M, Khan MA, Akram T et al (2017) A framework of human detection and action recognition based on uniform segmentation and combination of Euclidean distance and joint entropy-based features selection. Eurasip J Image Video Process. https://doi.org/10.1186/s13640-017-0236-8

    Article  Google Scholar 

  55. Singh R, Kushwaha AKS, Srivastava R (2019) Multi-view recognition system for human activity based on multiple features for video surveillance system. Multimed Tools Appl 78:17165–17196

    Google Scholar 

  56. Sahoo SP, Silambarasi R, Ari S (2019) Fusion of histogram based features for Human Action Recognition. In: 2019 5th international conference on advanced computing and communication systems, ICACCS 2019. IEEE, pp 1012–1016

  57. Gupta S, Ghosh Mazumdar S (2013) Sobel Edge detection algorithm. Int J Comput Sci Manag Res 2:1578–1583

    Google Scholar 

  58. Kolosnjaji B, Eckert C (2015) Neural network-based user-independent physical activity recognition for mobile devices. In: International conference on intelligent data engineering and automated learning. Springer, pp 378–386

  59. Kim Y-J, Kang B-N, Kim D (2015) Hidden Markov model ensemble for activity recognition using tri-axis accelerometer. In: 2015 IEEE international conference on systems, man, and cybernetics. IEEE, pp 3036–3041

  60. Acharjee D, Mukherjee A, Mandal JK, Mukherjee N (2016) Activity recognition system using inbuilt sensors of smart mobile phone and minimizing feature vectors. Microsyst Technol 22:2715–2722

    Google Scholar 

  61. Sousa W, Souto E, Rodrigres J, et al (2017) A comparative analysis of the impact of features on human activity recognition with smartphone sensors. In: Proceedings of the 23rd Brazillian Symposium on Multimedia and the Web. pp 397–404

  62. BhanuJyothi K, Bindu KH, Suryanarayana D (2017) A Comparative Study of Random Forest & K-Nearest Neighbors on HAR dataset Using Caret. IJIRT 3:6–9

    Google Scholar 

  63. Sikder N, Chowdhury MS, Arif AMS, Nahid A-A (2019) Human activity recognition using multichannel convolutional neural network. In: 2019 5th International conference on advanced electrical engineering

  64. Bhattacharya S, Shaw V, Singh PK, et al (2019) SV-NET: A deep learning approach to video based human activity recognition. In: Proceedings of the eleventh international conference on soft computing and pattern recognition, SoCPaR 2019. Hyderabad, India, pp 13–15

  65. Sadhukhan S, Mallick S, Singh PK et al (2020) A comparative study of different feature descriptors for video-based human action recognition. In: Mandal J, Banerjee S (eds) Intelligent computing: image processing based applications. Advances in intelligent systems and computing, vol 1157. Springer, Singapore. https://doi.org/10.1007/978-981-15-4288-6_3

  66. Rosati S, Balestra G, Knaflitz M (2018) Comparison of different sets of features for human activity recognition by wearable sensors. Sensors 18:4189

    Google Scholar 

  67. Zainudin MNS, SULAIMAN MDNBIN, Mustapha N et al (2018) Two-stage feature selection using ranking self-adaptive differential evolution algorithm for recognition of acceleration activity. Turkish J Electr Eng Comput Sci 26:1378–1389

    Google Scholar 

  68. Holland JH (1992) Genetic algorithms. Sci Am 1:66–73

    Google Scholar 

  69. Abualigah LMQ, Hanandeh ES (2015) Applying genetic algorithms to information retrieval using vector space model. Int J Comput Sci Eng Appl 5:19

    Google Scholar 

  70. Nezamabadi-Pour H (2015) A quantum-inspired gravitational search algorithm for binary encoded optimization problems. Eng Appl Artif Intell 40:62–75. https://doi.org/10.1016/j.engappai.2015.01.002

    Article  Google Scholar 

  71. Miller BL, Goldberg DE (1995) Genetic algorithms, tournament selection, and the effects of noise. Complex Syst 9:193–212

    MathSciNet  Google Scholar 

  72. Lipowski A, Lipowska D (2012) Roulette-wheel selection via stochastic acceptance. Phys A Stat Mech its Appl 391:2193–2196. https://doi.org/10.1016/j.physa.2011.12.004

    Article  Google Scholar 

  73. Guha R, Ghosh M, Singh PK et al (2019) M-HMOGA: a new multi-objective feature selection algorithm for handwritten numeral classification. J Intell Syst 29:1453–1467. https://doi.org/10.1515/jisys-2019-0064

    Article  Google Scholar 

  74. Guha R, Ghosh M, Kapri S et al (2019) Deluge based genetic algorithm for feature selection. Evol Intell. https://doi.org/10.1007/s12065-019-00218-5

  75. Ghosh M, Bera SK, Guha R, Sarkar R (2019) Contrast enhancement of degraded document image using partitioning based genetic algorithm. In: International conference on emerging technologies for sustainable development (ICETSD’19). pp 431–435

  76. Davis M, Maschler M (1965) The kernel of a cooperative game. Nav Res Logist Q 12:223–259

    MathSciNet  MATH  Google Scholar 

  77. Bilbao JM (2012) Cooperative games on combinatorial structures. Springer, Berlin

    MATH  Google Scholar 

  78. Mukaka MM (2012) A guide to appropriate use of correlation coefficient in medical research. Malawi Med J 24:69–71

    Google Scholar 

  79. Lawrence I, Lin K (1989) A concordance correlation coefficient to evaluate reproducibility. Biometrics 45(1):255–268

    MATH  Google Scholar 

  80. Shrout PE, Fleiss JL (1979) Intraclass correlations: uses in assessing rater reliability. Psychol Bull 86:420

    Google Scholar 

  81. Guha R, Ghosh KK, Bhowmik S, Sarkar R (2020) Mutually informed correlation coefficient (MICC)—a new filter based feature selection method. In: 2020 IEEE Calcutta conference (CALCON), Kolkata, India, pp 54–58. https://doi.org/10.1109/CALCON49167.2020.9106516

  82. Estévez PA, Tesmer M, Perez CA, Zurada JM (2009) Normalized mutual information feature selection. IEEE Trans Neural Netw 20:189–201

    Google Scholar 

  83. Amiri F, Yousefi MR, Lucas C et al (2011) Mutual information-based feature selection for intrusion detection systems. J Netw Comput Appl 34:1184–1199

    Google Scholar 

  84. Elgammal A, Duraiswami R, Harwood D, Davis LS (2002) Background and foreground modeling using nonparametric kernel density estimation for visual surveillance. Proc IEEE 90:1151–1163

    Google Scholar 

  85. Ngiam J, Koh PW, Chen Z, Bhaskar SA, Ng AY (2011) Sparse filtering. NIPS 11:1125–1133

    Google Scholar 

  86. Dua, D. and Graff C (2019) UCI Machine Learning Repository. In: Irvine, CA Univ. California, Sch. Inf. Comput. Sci. http://archive.ics.uci.edu/ml. Accessed 7 Jan 2019

  87. Basu S, Das N, Sarkar R − , et al (2005) Handwritten ‘Bangla’ alphabet recognition using an MLP based classfier. In: 2nd National conference on computer processing of Bangla-2005. pp 285–291

  88. Siedlecki W, Sklansky J (1993) A note on genetic algorithms for large-scale feature selection. Handbook of pattern recognition and computer vision. World Scientific, vol 10, pp 88–107

  89. Kennedy J, Eberhart RC (1997) Discrete binary version of the particle swarm algorithm. In: Proceedings of the IEEE international conference on systems, man and cybernetics. pp 4104–4108

  90. Rashedi E, Nezamabadi-Pour H, Saryazdi S (2010) BGSA: binary gravitational search algorithm. Nat Comput 9:727–745. https://doi.org/10.1007/s11047-009-9175-3

    Article  MathSciNet  MATH  Google Scholar 

  91. Ghosh M, Guha R, Alam I et al (2019) Binary genetic swarm optimization: a combination of GA and PSO for feature selection. J Intell Syst 29:1598–1610

    Google Scholar 

  92. Ghosh M, Guha R, Sarkar R, Abraham A (2019) A wrapper-filter feature selection technique based on ant colony optimization. Neural Comput Appl. https://doi.org/10.1007/s00521-019-04171-3

    Article  Google Scholar 

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Appendix

Appendix

1.1 Pearson Correlation Coefficient

Pearson correlation coefficient (PCC) is a correlation measure that is used in statistics to find the strength of linear dependence between two random variables \( x \) and \( y \). It is denoted by the following equation:

$$ r_{xy} = \frac{{\mathop \sum \nolimits_{i = 1}^{n} \left( {x_{i} - \bar{x}} \right)\left( {y_{i} - \bar{y}} \right)}}{{\sqrt {\mathop \sum \nolimits_{i = 1}^{n} \left( {x_{i} - \bar{x}} \right)^{2} } \sqrt {\mathop \sum \nolimits_{i = 1}^{n} \left( {y_{i} - \bar{y}} \right)^{2} } }} $$
(1)

where \( r_{xy} \) is the PCC value, \( x_{i} \) and \( y_{i} \) represents \( i{\text{th}} \) sample in \( x \) and \( y, \) respectively, and \( n \) is the total number of samples.

In the present context, we have considered different features as random variables. From the equation, we know that the PCC gives a real value between − 1 to +1 for any two features. A value lesser than 0 means both the features are inversely dependent on each other, i.e., if one variable increases, others will decrease. A value equal to 0 means both the features are independent of each other. A value greater than 0 signifies, they both are directly dependent on each other, i.e., if one feature increases, then the other will also increase.

1.2 Mutual information

Mutual information (MI) is a measure between two (possibly multi-dimensional) random variables \( X \) and \( Y \) that quantifies the amount of information obtained about one random variable, through the other random variable. The mutual information is given by

$$ I\left( {X;Y} \right) = \mathop \sum \limits_{x \in X,y \in Y} P_{xy} \left( {x,y} \right)log\frac{{P_{xy} \left( {x,y} \right)}}{{P_{x} \left( x \right)P_{y} \left( y \right)}} $$
(2)

where \( P_{xy} \left( {x,y} \right) \) is the joint probability density function of \( X \) and \( Y \), and \( P_{x} \left( x \right) \) and \( P_{y} \left( y \right) \) are the marginal density functions. The mutual information determines how similar the joint distribution \( P_{xy} \left( {x,y} \right) \) is to the products of the factored marginal distributions. If X and Y are completely unrelated (and therefore independent), then \( P_{xy} \left( {x,y} \right) \) would equal \( P_{x} \left( x \right)P_{y} \left( y \right) \), and this integral would be zero.

1.3 Cooperative game theory

In the world of game theory, cooperative game (also known as coalition game) is a well-known game that revolves around the formation of groups or coalitions of players depending on collective payoffs. Let there be \( n\left( { > = 2} \right) \) players playing the game. \( N \) represents the entire set of players as \( \{ 1, \, 2, \, 3 \ldots n\} . \) A coalition is defined as a subset \( C \subset N \). So, there are \( 2^{n} \) ways of selecting a coalition. A formal definition of the coalition can be found in [68] as:

The coalitional form of an \( n \)-person game is given by the pair (\( N,f \)), where \( N = \{ 1, \, 2, \ldots ,n\} \) is the set of players and \( f \) is a real-valued function, called the characteristic function of the game, defined on the set, \( 2^{N} \), of all coalitions (subsets of \( N \)), and satisfying

  1. i.

    \( f(\emptyset ) \, = \, 0, \) where \( \emptyset \) is the empty coalition.

  2. ii.

    (super-additivity) if \( S \) and T are disjoint coalitions (\( (S \cap T = \emptyset ), \) then \( f(S) + f(T) \le f(S \cup T). \)

The value \( f(C) \) is a real value for any coalition \( C \subset N \). It gives us a measure of strength or worth possessed by the coalition. So, the ultimate purpose of the game becomes to find a worthy coalition of players subject to some application-related constraints.

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Guha, R., Khan, A.H., Singh, P.K. et al. CGA: a new feature selection model for visual human action recognition. Neural Comput & Applic 33, 5267–5286 (2021). https://doi.org/10.1007/s00521-020-05297-5

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