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.
Citations
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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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20 Jun 2009
TL;DR: A learning based approach for the automatic annotation of visually deformable objects from a single annotated frontal image is presented and demonstrated on the example of automatically annotating face images that can be used for building AAMs for fitting and tracking.
Abstract: Statistical approaches for building non-rigid deformable models, such as the active appearance model (AAM), have enjoyed great popularity in recent years, but typically require tedious manual annotation of training images. In this paper, a learning based approach for the automatic annotation of visually deformable objects from a single annotated frontal image is presented and demonstrated on the example of automatically annotating face images that can be used for building AAMs for fitting and tracking. This approach employs the idea of initially learning the correspondences between landmarks in a frontal image and a set of training images with a face in arbitrary poses. Using this learner, virtual images of unseen faces at any arbitrary pose for which the learner was trained can be reconstructed by predicting the new landmark locations and warping the texture from the frontal image. View-based AAMs are then built from the virtual images and used for automatically annotating unseen images, including images of different facial expressions, at any random pose within the maximum range spanned by the virtually reconstructed images. The approach is experimentally validated by automatically annotating face images from three different databases.
16 citations
01 May 2011
TL;DR: An image annotation approach by incorporating word correlations into multi-class support vector machine (SVM) and the probabilistic outputs from SVM and the word correlations are integrated to obtain the final annotation keywords is proposed.
Abstract: Image annotation systems aim at automatically annotating images with semantic keywords. Machine learning approaches are often used to develop these systems. In this paper, we propose an image annotation approach by incorporating word correlations into multi-class support vector machine (SVM). At first, each image is segmented into five fixed-size blocks instead of time-consuming object segmentation. Every keyword from training images is manually assigned to the corresponding block and word correlations are computed by a co-occurrence matrix. Then, MPEG-7 visual descriptors are applied to these blocks to represent visual features and the minimal-redundancy-maximum-relevance (mRMR) method is used to reduce the feature dimension. A block-feature-based multi-class SVM classifier is trained for 80 semantic concepts. At last, the probabilistic outputs from SVM and the word correlations are integrated to obtain the final annotation keywords. The experiments on Corel 5000 dataset demonstrate our approach is effective and efficient.
15 citations
"Annotating Dance Posture Images Usi..." refers background in this paper
...Most of the works on using texture for image representation have considered global texture features [19], [20], [21]....
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09 Jul 2007
TL;DR: The novel use of multiple segmentations in the context of image auto-annotation is incorporated into a region based image annotation technique proposed in previous work, namely the training image based feature space approach.
Abstract: Automatic image annotation techniques that try to identify the objects in images usually need the images to be segmented first, especially when specifically annotating image regions. The purpose of segmentation is to separate different objects in images from each other, so that objects can be processed as integral individuals. Therefore, annotation performance is highly influenced by the effectiveness of segmentation. Unfortunately, automatic segmentation is a difficult problem, and most of the current segmentation techniques do not guarantee good results. A multiple segmentations algorithm is proposed by Russell et al. [12] to discover objects and their extent in images. In this paper, we explore the novel use of multiple segmentations in the context of image auto-annotation. It is incorporated into a region based image annotation technique proposed in previous work, namely the training image based feature space approach. Three different levels of segmentations were generated for a 5000 image collection. Experimental results show that image auto-annotation achieves better performance when using all three segmentation levels together than using any single one on its own.
7 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]....
[...]
14 Aug 2009
TL;DR: An automatic image annotation approach by incorporating word correlations into multi-class Support Vector Machine (SVM) and the minimal-redundancy-maximum-relevance (mRMR) method is used to reduce feature dimensions.
Abstract: Image annotation systems aim at automatically annotating images with some predefined keywords. In this paper, we propose an automatic image annotation approach by incorporating word correlations into multi-class Support Vector Machine (SVM). At first, each image is segmented into five fixed-size blocks or tiles and MPEG-7 visual descriptors are applied to represent color and texture features of blocks. Keywords are manually assigned to every block of training images. Then, multi-class SVM classifier is trained for semantic concepts. Word or concept correlations are computed by a co-occurrence matrix. The probability outputs from SVM and word correlations are combined to obtain the final results. The minimal-redundancy-maximum-relevance (mRMR) method is used to reduce feature dimensions. The experiments on Corel 5000 dataset demonstrate our approach is effective and efficient.
7 citations
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05 Jul 2007
2 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]....
[...]