Indian Classical Dance Classification with Adaboost Multiclass Classifier on Multifeature Fusion

Kumar, Kaushal; Kishore, P. V. V.; Kumar, D. Anil

doi:10.1155/2017/6204742

Cited by 30 publications

(15 citation statements)

References 37 publications

(46 reference statements)

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“…But real-time implementation of ICD identification demands a high-speed classifier. To improve the speed of the recognition, Adaboost classifier [14] is introduced. Even though the recognition is fast, the classification results were found to be somewhat unreliable at times.…”

Section: Resultsmentioning

confidence: 99%

“…In our previous work, we approached the problem with SVM classifier [13] on dance videos and found that only multiclass SVMs should be considered. The average recognition rates obtained in implementing Adaboost [14], Artificial Neural Networks (ANN) [15], deep ANN [16], and adaptive graph matching (AGM) [17] on dance data are not up to the mark.…”

Section: Literature Reviewmentioning

confidence: 99%

“…For faster recognition, we used SVM in [13] and ended with very low classification rates. Further, we replaced SVM with Adaboost classifier [14] and found moderate recognition rates. Later, we used adaptive graph matching (AGM) model on the data and found good recognition rates.…”

Section: Batch Iii: Cnn Training With Three Sets Of Datamentioning

confidence: 99%

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Indian Classical Dance Action Identification and Classification with Convolutional Neural Networks

Kishore

Kumar

et al. 2018

Advances in Multimedia

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Extracting and recognizing complex human movements from unconstrained online/offline video sequence is a challenging task in computer vision. This paper proposes the classification of Indian classical dance actions using a powerful artificial intelligence tool: convolutional neural networks (CNN). In this work, human action recognition on Indian classical dance videos is performed on recordings from both offline (controlled recording) and online (live performances, YouTube) data. The offline data is created with ten different subjects performing 200 familiar dance mudras/poses from different Indian classical dance forms under various background environments. The online dance data is collected from YouTube for ten different subjects. Each dance pose is occupied for 60 frames or images in a video in both the cases. CNN training is performed with 8 different sample sizes, each consisting of multiple sets of subjects. The remaining 2 samples are used for testing the trained CNN. Different CNN architectures were designed and tested with our data to obtain a better accuracy in recognition. We achieved a 93.33% recognition rate compared to other classifier models reported on the same dataset.

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Section: Resultsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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Indian Classical Dance Action Identification and Classification with Convolutional Neural Networks

Kishore

Kumar

et al. 2018

Advances in Multimedia

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“…To further know the robustness and efficiency of flower image classification with the proposed CNN, it is compared with other classifiers. For faster recognition, we used Adaboost classifier [40] and ended with a very low classification rates. Further, we replaced Adaboost with a traditional artificial neural network (ANN) [35] [36] [37] [38] for flower image classification and found better recognition rates.…”

Section: Cnn Training and Testing With Different Dataset In Various Bmentioning

confidence: 99%

An efficient classification of flower images with convolutional neural networks

Prasad¹,

JwalaLakshmamma²,

Chandana³

et al. 2017

IJET

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Machine learning is penetrating most of the classification and recognition tasks performed by a computer. This paper proposes the classification of flower images using a powerful artificial intelligence tool, convolutional neural networks (CNN). A flower image database with 9500 images is considered for the experimentation. The entire database is sub categorized into 4. The CNN training is initiated in five batches and the testing is carried out on all the for datasets. Different CNN architectures were designed and tested with our flower image data to obtain better accuracy in recognition. Various pooling schemes were implemented to improve the classification rates. We achieved 97.78% recognition rate compared to other classifier models reported on the same dataset.

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“…In [1] SVMC is firstly introduced and verified with set of benchmark dataset of image and it is used in this work and they yield a major furtherance new fusion model and result outcome is based on qualitative and quantity n datasets. There is major investigation is carried out in [2] about shapes and large pixel size of image which is taken into account by using a algorithm of segmented interactive classifier and in this present proposed work used this technology for accurate and sharp identification images in the sets. The new superior computer vision model [3] is yield with aim to help for e-learning and self learning.…”

Section: Introductionmentioning

confidence: 99%

Recognition and Classification of Various Dance Forms by Using SVM Classifier

Maale¹,

Pushpanjali²

2019

IJRAT

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Indian traditional dance involves a tough and difficult dance poses and hand gesture and leg movements, so it is a tedious task to recognize and discover human dance movements from video sequences because dance poses are hidden in their heavy makeup and typical costumes and it make nervures for audience who watching dance performance and also to dance fans. In this paper work we have taken into account this problem and made an improvement by using a new classification model. In this new model we demonstrated three varieties of classical dance forms such as Bharathanatium, oddisi, kathak. In Research lab we experimented various form of dance videos and simultaneously carried an investigation on frames (images) generated from uploaded video by using a python integrated shell environment. The new model used a Shannon entropy algorithm for preprocessing of particular dance image and applied a Hu movements for extract a features and this will be issued as input to SVM classifier and then it predict which dance form it is and at same time it display a pixel value of that image with this new model system performance is high recognition accuracy is 94.4% with SVM classifier.

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Indian Classical Dance Classification with Adaboost Multiclass Classifier on Multifeature Fusion

Cited by 30 publications

References 37 publications

Indian Classical Dance Action Identification and Classification with Convolutional Neural Networks

Indian Classical Dance Action Identification and Classification with Convolutional Neural Networks

An efficient classification of flower images with convolutional neural networks

Recognition and Classification of Various Dance Forms by Using SVM Classifier

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