Classification of Indian Dance Forms using Pre-Trained Model-VGG

Biswas, Snigdha; Ghildiyal, Anirudh; Sharma, Sachin

doi:10.1109/wispnet51692.2021.9419426

Cited by 10 publications

(2 citation statements)

References 6 publications

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“…This ordinary improvement is enhanced by 3D point clouds using the recurrence condition of neural networks [27]. Improvements were proposed by applying image pre-processing of dance video frames and then extracting features such as motion information [28], dancer parts [29], body shapes [30] and global automated features using conventional layers [29], [31]. All the above models used either CNN dense layers for classification or represented frame-level features as time series information using recurrent neural networks.…”

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

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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.

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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

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“…[39] proposed a classification of different dance forms into 8 classes is attempted using a deep convolutional neural network (DCNN) model using backbone as ResNet50, which results the classification accuracy of 91.1%. [40] has been proposed a transfer-leaning based CNN Model for ICD classification. This paper used VGG-16 and VGG-19 pre-trained models for ICD classification.…”

Section: State-of-art Comparisonmentioning

confidence: 99%

A novel approach for optimization of convolution neural network with hybrid particle swarm and grey wolf algorithm for classification of Indian classical dances

Challapalli

Devarakonda

2022

Knowl Inf Syst

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Deep learning is the most dominant area to perform the complex challenging tasks such as image classification and recognition. Earlier researchers have been proposed various convolution neural network (CNN) with different architectures to improve the performance accuracy for the classification and recognition of images. However, the fine-tuning of hyper parameters, resulting the optimal network, regularization of parameters is the difficult task. The metaheuristic optimization algorithms are used for solving such kind of problems. In this paper we proffer a fine tune automate CNN with Hybrid Particle Swarm Grey Wolf (HPSGW). This novel algorithm used to discover the optimal parameters of the CNN like batch size, number of hidden layers, number of epochs and size of filters. The proffered optimized architecture is implemented on MNIST, CIFAR are two bench mark datasets and Indian Classical Dance (ICD) for the classification of 8 Indian Classical Dances. The Proffered method improves the model performance accuracy of 97.3% on ICD Dataset, and other benchmark datasets MNIST, CIFAR with an improved accuracy of 99.4% and 91.1%. This auto-tuned network improved the performance by 5.6% for Indian Classical Dance Forms Classification compared to earlier methods and also reduces the computational cost.

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