Optimal feature selection and classification of Indian classical dance hand gesture dataset

Raj, Reshma; Dharan, Smitha; Sunil, T. T.

doi:10.1007/s00371-022-02572-5

Cited by 5 publications

(1 citation statement)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This has improved the recognition accuracy due to the influence of pre-trained convolutional layers. Instead of focusing on automated feature classification with a dense layer of CNN, feature engineering has been initiated with automated CNN or CNN-RNN variations [34] which are then classified with machine learning methods such as k-nearest neighborhood, Bayes, fuzzy and SVM [35]. A slight enhancement in accuracy has been achieved by using a capsule network for training and testing on dance image data [36].…”

Section: Literature Reviewmentioning

confidence: 99%

Machine Interpretation of Ballet Dance: Alternating Wavelet Spatial and Channel Attention Based Learning Model

Kishore,

Kumar,

Kumar

et al. 2024

IEEE Access

View full text Add to dashboard Cite

Ballet' is a 15 th -century concert performing dance form that originated in Italy. Current AI models for ballet dance pose identification in live performance videos is challenging due to variational pixel distribution of human actions across backgrounds. Notably, their performance on online video datasets improved with both channel (CA) and spatial attention (SA) models but tend to generate over-smoothed Convolutional features due to feature averaging in the attention network. Alternatively, wavelet attention preserves both high and low frequency components in the features which improves the test accuracy. Applying CA and SA on wavelet features simultaneously resulted in hyper-refined features due to double averaging. To overcome this drawback, Alternating Wavelet Channel and Spatial Attention (AWCSA) across any learning network as backbone architecture is proposed. The global features across the residual connections in the backbone (ResNet50) are amplified exclusively with low and high-frequency local features across the channel and spatial dimensions alternatively one after the other. The Ballet online dance video dataset (BOVD23) evaluates the performance of the proposed AWCSA along with baseline action datasets. The end-to-end trained AWCSA has recorded a 6-8% higher performance metrics on BOVD23 dataset over the counterparts.

show abstract

Section: Literature Reviewmentioning

confidence: 99%