Classifying Indian Classical Dances By Motion Posture Patterns

Dance is a classic form of human motion which is usually performed as a reaction of expression to music. The Indian classical dances, for instance, require multiple complicated movements that relates to body motion postures and hand gestures with high similarities. Past studies showed interests using various methods to classify dances. The most common method used is the Hidden Markov Models (HMM), apart from using the correlation matrix method and hierarchical cluster analysis. Nevertheless, less effort has been placed in analysing the Indian dance by using the data mining approach. Therefore, the objectives in this work are to (i) distinguish different types of Indian classical dances, (ii) classify the type of dance based on motion posture patterns and (iii) determine the effects of attributes on the classification accuracy. This study involves five types of Indian classical dances (Kathak, Bharatanatyam, Kuchipudi, Manipuri and Odissi) motion postures. The data mining approaches were used to classify the motion posture patterns by type of dances. A total of 15 dance videos were collected from the public available domain for body joints tracking processes using the Kinovea software. Data mining analysis was performed in three stages: data pre�processing, data classification and knowledge discovery using the WEKA software. RandomForest algorithm returned the highest classification accuracy (99.2616%). On attribute configuration, y-coordinates of left wrist (LW(y)) was identified as the most significant attribute to differentiate the Indian classical dance classes

Deep Architectures for Visual Recognition and Description

In recent times, digital media contents are inherently of multimedia type, consisting of the form text, audio, image and video. Several of the outstanding computer Vision (CV) problems are being successfully solved with the help of modern Machine Learning (ML) techniques. Plenty of research work has already been carried out in the field of Automatic Image Annotation (AIA), Image Captioning and Video Tagging. Video Captioning, i.e., automatic description generation from digital video, however, is a different and complex problem altogether. This study compares various existing video captioning approaches available today and attempts their classification and analysis based on different parameters, viz., type of captioning methods (generation/retrieval), type of learning models employed, the desired output description length generated, etc. This dissertation also attempts to critically analyze the existing benchmark datasets used in various video captioning models and the evaluation metrics for assessing the final quality of the resultant video descriptions generated. A detailed study of important existing models, highlighting their comparative advantages as well as disadvantages are also included. In this study a novel approach for video captioning on the Microsoft Video Description (MSVD) dataset and Microsoft Video-to-Text (MSR-VTT) dataset is proposed using supervised learning techniques to train a deep combinational framework, for achieving better quality video captioning via predicting semantic tags. We develop simple shallow CNN (2D and 3D) as feature extractors, Deep Neural Networks (DNNs and Bidirectional LSTMs (BiLSTMs) as tag prediction models and Recurrent Neural Networks (RNNs) (LSTM) model as the language model. The aim of the work was to provide an alternative narrative to generating captions from videos via semantic tag predictions and deploy simpler shallower deep model architectures with lower memory requirements as solution so that it is not very memory extensive and the developed models prove to be stable and viable options when the scale of the data is increased. This study also successfully employed deep architectures like the Convolutional Neural Network (CNN) for speeding up automation process of hand gesture recognition and classification of the sign languages of the Indian classical dance form, ‘Bharatnatyam’. This hand gesture classification is primarily aimed at 1) building a novel dataset of 2D single hand gestures belonging to 27 classes that were collected from (i) Google search engine (Google images), (ii) YouTube videos (dynamic and with background considered) and (iii) professional artists under staged environment constraints (plain backgrounds). 2) exploring the effectiveness of CNNs for identifying and classifying the single hand gestures by optimizing the hyperparameters, and 3) evaluating the impacts of transfer learning and double transfer learning, which is a novel concept explored for achieving higher classification accuracy

Scientist - performers - audiences. Different modes of meaning-making

This paper provides a brief overview of the emergence of dance in the field of cognitive neuroscience and sustains the importance to understand-ing how dance is conceptualised in other disciplines in order to design valuable future research employing dance. It is proposed that recognising the distinct modes of meaning-making will undoubtedly affect and advance the scientific progress understanding by modes of meaning-making the phenomenological difference in how a dancer relates to a movement phrase from how a scientist or a choreographer watches, interprets, and experiences a dance phrase. The overall message is that the different modes of meaning-making requires consideration in scientific studies where a new generation of artists-scientists is needed, driven to excel in both, data handling and artistic purposes.//Abstract in Spanish:Este artículo proporciona una breve descripción de la aparición de la danza en el campo de la neurociencia cognitiva y sostiene que es importante entender cómo se conceptualiza la danza en otras disciplinas para diseñar in-vestigaciones futuras que la empleen. Se propone que reconocer los distintos modos de creación de significado afectará y avanzará el progreso científico entendiendo por modos de creación de significado la diferencia fenomenológica en la forma en que un bailarín se relaciona con una frase de movimiento de cómo lo hace un científico o un coreógrafo. El mensaje general es que los dif-erentes modos de creación de significado requieren consideración en estudios científicos donde son necesarios una nueva generación de artistas-científicos, impulsados a sobresalir tanto en el manejo de datos como en los propósitos artísticos

Dance Gesture Recognition Using Space Component And Effort Component Of Laban Movement Analysis

Dance is a collection of gestures that have many meanings. Dance is a culture that is owned by every country whose every movement has beauty or meaning contained in the dance movement. One obstacle in the development of dance is to recognize dance moves. In the process of recognizing dance movements one of them is information technology by recording motion data using the Kinect sensor, where the results of the recording will produce a motion data format with the Biovision Hierarchy (BVH) file format. BVH motion data have position compositions (x, y, z). The results of the existing dance motion record will be extracted features using Laban Movement Analysis (LMA), where the LMA has four main components namely Body, Shape, Space, and Effort. After extracting the features, quantization, normalization, and classification will be performed. Using Hidden Markov Model (HMM). In this study using two LMA components, namely Space and Effort in extracting features in motion recognition patterns. From the results of the test and the resulting accuracy is approaching 99% for dance motion data

Diálogos com a arte. Revista de arte, cultura e educação, nº 6

The journal " Diálogos com a Arte. Revista de Arte, Cultura e Educação " is an indexed annual journal of international circulation, published since 2010, and edited by the School of Education of the Polytechnic Institute of Viana do Castelo (ESE-IPVC) in collaboration with the Center for Research in Child Studies of the University of Minho (CIEC-UM). The journal offers students, teachers and researchers in the arts the possibility of reflecting on both national and international theories and practices about art, culture and education The editorial board defines cooperation as a form of cultural activism that necessitates acting on problems and sharing actions and experiences. Cooperation is successfully accomplished when all the participants’ objectives are shared and the results are beneficial for everyone. This requires constant dialogue and ensuring relations in educational programs, projects, community interventions, artistic and cultural training, and teacher education.A revista “Diálogos com a Arte. Revista de Arte, Cultura e Educação” é uma revista anual indexada, de circulação internacional, publicada desde 2010, e editada pela Escola Superior de Educação do Instituto Politécnico de Viana do Castelo (ESSE-IPVC) em colaboração com o Centro de Investigação em Estudos da Criança da Universidade do Minho (CIEC-UM). A revista oferece a alunos, professores e investigadores no campo das artes a possibilidade de reflexão sobre teorias e práticas artísticas, culturais e educacionais nos âmbitos nacional e internacional. A equipa editorial define a cooperação como uma forma de activismo cultural que precisa de acção sobre os problemas e de partilha de experiências. A cooperação é alcançada com sucesso quando todos os objectivos dos participantes são partilhados e os resultados são benéficos para todos. Isto exige uma diálogo constante e a garantia do estabelecimento de relações entre programas educacionais, projectos, intervenções comunitárias, formação artística e cultural e formação de professores