Annotating Dance Posture Images Using Multi Kernel Feature Combination
15 Dec 2011-pp 41-45
TL;DR: A novel dance posture based annotation model by combining features using Multiple Kernel Learning (MKL) and a novel feature representation which represents the local texture properties of the image is proposed.
Abstract: We present a novel dance posture based annotation model by combining features using Multiple Kernel Learning (MKL). We have proposed a novel feature representation which represents the local texture properties of the image. The annotation model is defined in the direct a cyclic graph structure using the binary MKL algorithm. The bag-of-words model is applied for image representation. The experiments have been performed on the image collection belonging to two Indian classical dances (Bharatnatyam and Odissi). The annotation model has been tested using SIFT and the proposed feature individually and by optimally combining both the features. The experiments have shown promising results.
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03 Sep 2012
TL;DR: A computational model to represent BN dance steps is proposed as a SMart system for modelling BN steps, where SMart stands for System Modelled art and the detailed description of formulation of a dance position vector that comprises of thirty explicitly identified attributes is presented.
Abstract: BharataNatyam (BN) like any other Indian classical dance comprises of a sequence of possible and legitimate dance steps. It is estimated that using the main body parts namely head, neck, hand and leg itself, more than 5 lakh dance steps can be generated for a single beat. Choreographers and even dancers usually repeat their favorite dance steps or the conventional casual dance steps taught by their teacher while performing for multiple beats. As a result several valid and many other significant non-traditional dance steps remain unexplored. Hence, we propose to have an auto enumeration followed by auto classification of significant BN dance steps that can be used in dance performance and choreography. In short, we try to transform sheer art into a System Modelled art i.e. 'Art to SMart'. The foremost and most challenging task is to have a computational model that represents different BN dance poses. In this paper, we have proposed a computational model to represent BN dance steps and have presented the detailed description of formulation of a dance position vector that comprises of thirty explicitly identified attributes to capture and represent all variations of a BN dance step. We have named it as a SMart system for modelling BN steps, where SMart stands for System Modelled art. We have also demonstrated sample dance steps and their corresponding representations with appropriate dance step images.
14 citations
TL;DR: The obtained results support a general acceptance towards ARDTS among the users who are interested in exploring the cutting-edge technology of AR for gaining expertise in a dance skill.
Abstract: The advancement in Computer Vision (CV) has evolved drastically from image processing to object recognition, tracking video, restoration of images, three-dimensional (3D) pose recognition, and emotion analysis These advancements have eventually led to the birth of Augmented Reality (AR) technology, which means embedding virtual objects into the real-world environment The primary focus of this research was to solve the long-term learning retention and poor learning efficiency for mastering a dance skill through the AR technology based on constructivism learning theory, Dreyfus model and Technology Acceptance Model (TAM) The problem analysis carried out in this research had major research findings, in which the retention and learning efficiency of a dance training system were predominantly determined through the type of learning theory adopted, learning environment, training tools, skill acquisition technology and type of AR technique Therefore, the influential factors for the user acceptance of AR-based dance training system (ARDTS) were based on quantitative analysis These influential factors were determined to address the problem of knowledge gap on acceptance of AR-based systems for dance education through self-learning The evaluation and testing were conducted to validate the developed and implemented ARDTS system The Technology Acceptance Model (TAM) as the evaluation model and quantitative analysis was done with a research instrument that encompassed external and internal variables TAM consisted of 37 items, in which six factors were used to assess the new developed ARDTS by the authors and its acceptability among 86 subjects The current study had investigated the potential use of AR-based dance training system to promote a particular dance skill among a sample population with various backgrounds and interests The obtained results support a general acceptance towards ARDTS among the users who are interested in exploring the cutting-edge technology of AR for gaining expertise in a dance skill
13 citations
8 citations
01 Jan 2012
TL;DR: The empiric results showed that probabilistic grammar-based classifiers that were induced using the Alergia algorithm with Symbolic Aggregate Approximation (SAX) discretization method achieved 92% of average precision in recognizing a predefined set of dance gestures.
Abstract: Automatic recognition of dance gesture is one important research area in computer vision with many potential applications. Bali traditional dance comprises of many dance gestures that relatively unchanged over the years. Although previous studies have reported various methods for recognizing gesture, to the best of our knowledge, a method to model and classify dance gesture of Bali traditional dance is still unfound in literature. The aim of this paper is to build a robust recognizer based on linguistic motivated method to recognize dance gesture of Bali traditional dance choreography. The empiric results showed that probabilistic grammar-based classifiers that were induced using the Alergia algorithm with Symbolic Aggregate Approximation (SAX) discretization method achieved 92% of average precision in recognizing a predefined set of dance gestures. The study also showed that the most discriminative features to represent Bali traditional dance gestures are skeleton joint features of: left/right
8 citations
References
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24 Aug 2008
TL;DR: A nonparametric Bayesian model which provides a generalization for multi-model latent Dirichlet allocation model (MoM-LDA) used for similar problems in the past and performs just as well as or better than the MoM- LDA model (regardless of the choice of the number of clusters) for predicting labels of objects in images containing multiple objects.
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59 citations
"Annotating Dance Posture Images Usi..." refers background in this paper
...The adaptation of such latent space models for image annotation have been demonstrated in [15],[16]....
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30 Oct 2000
TL;DR: In this demo, the efficiency of the semi-automatic image annotation method and its help in improving the image retrieval accuracy is shown.
Abstract: MiAlbum© (v1.0) is a system developed at Microsoft Research China for managing family photos with the help of a semi-automatic image annotation approach. It provides functionalities to import (from scanner, digital camera, disks, PC, CD, web, etc), label (keyword, name, place, etc.), categorize (by automatic classification into some predefined classes), manipulate (in some ways), and export (send or print) family photo images. In this demo, we show the efficiency of the semi-automatic image annotation method and its help in improving the image retrieval accuracy.
54 citations
"Annotating Dance Posture Images Usi..." refers background in this paper
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09 Jul 2007
TL;DR: A probabilistic approach to refine image annotations by incorporating semantic relations between annotation words using a conditional random field (CRF) model where each vertex indicates the final decision on a candidate annotation word.
Abstract: In this paper, we present a probabilistic approach to refine image annotations by incorporating semantic relations between annotation words. Our approach firstly predicts a candidate set of annotation words with confidence scores. This is achieved by the relevance vector machine (RVM), which is a kernel based probabilistic classifier in order to cope with nonlinear classification. Given the candidate annotations, we model semantic relationships between words using a conditional random field (CRF) model where each vertex indicates the final decision (true / false) on a candidate annotation word. The refined annotation is given by inferring the most likely states of these vertexes. In the CRF model, we consider the confidence scores given by the RVM classifiers as local evidences. In addition, we utilise Normalized Google distances (NGD's) between two words as their contextual potential. NGD is a distance function between two words obtained by searching a pair of words using the Google search engine. It has a simple mathematical formulation with a foundation in Kolmogorov theory. We also propose a learning algorithm to tune the weight parameters in the CRF model. These weight parameters control the balance between the local evidence of a single word and the contextual relation between words. Our experiments on the Corel images demonstrate the effect of our approach.
38 citations
"Annotating Dance Posture Images Usi..." refers background in this paper
...Some of the recent works have attempted segmentation based annotation model [3], [4], [5]....
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30 Oct 2008
TL;DR: This work uses MOWL, a multimedia extension of Web Ontology Language (OWL) which is capable of describing domain concepts in terms of their media properties and of capturing the inherent uncertainties involved.
Abstract: In this work, we offer an approach to combine standard multimedia analysis techniques with knowledge drawn from conceptual metadata provided by domain experts of a specialized scholarly domain, to learn a domain-specific multimedia ontology from a set of annotated examples. A standard Bayesian network learning algorithm that learns structure and parameters of a Bayesian network is extended to include media observables in the learning. An expert group provides domain knowledge to construct a basic ontology of the domain as well as to annotate a set of training videos. These annotations help derive the associations between high-level semantic concepts of the domain and low-level MPEG-7 based features representing audio-visual content of the videos. We construct a more robust and refined version of this ontology by learning from this set of conceptually annotated videos. To encode this knowledge, we use MOWL, a multimedia extension of Web Ontology Language (OWL) which is capable of describing domain concepts in terms of their media properties and of capturing the inherent uncertainties involved. We use the ontology specified knowledge for recognizing concepts relevant to a video to annotate fresh addition to the video database with relevant concepts in the ontology. These conceptual annotations are used to create hyperlinks in the video collection, to provide an effective video browsing interface to the user.
20 citations
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01 Jan 2009
17 citations