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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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.
11 citations
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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
7 citations
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TL;DR: P pose recognition is performed for some important postures in Bharatnatyam in order to find the origin of these postures from the Bhangas and further use this result to predict the expertise of a Bharat natyam dancer.
Abstract: Bharatnatyam is an ancient Indian Classical Dance form consisting of complex postures and movements. One main challenge which has not been addressed till now in the intelligent systems community is to perform pose recognition for the basic postures of this dance form called the Bhangas and use this for expertise prediction. In this paper, pose recognition is performed for some important postures in Bharatnatyam in order to find the origin of these postures from the Bhangas and further use this result to predict the expertise of a Bharatnatyam dancer. The features extracted are 10 joint angles using 15 joint locations to predict the 22 postures derived from the basic postures (Bhangas). Support Vector Machine classifier with a radial basis function kernel performed the best for pose recognition. By performing stick figure analysis and grouping of labels we estimate the origin of each of these postures from the Bhangas. This is followed by verification of the grouping using Hamming distance calculation. Testing is done on our own Bharatnatyam dataset consisting of 102 dancers, achieving an accuracy of 87.14%. Expertise prediction of the dancers for the 22 poses was performed for four ratings - Excellent, Good, Satisfactory and Poor giving an accuracy of 68.46% without grouping of postures and 80.80% with grouping of postures.
1 citations
References
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TL;DR: Experimental results show that robust object recognition can be achieved in cluttered partially occluded images with a computation time of under 2 seconds.
Abstract: An object recognition system has been developed that uses a new class of local image features. The features are invariant to image scaling, translation, and rotation, and partially invariant to illumination changes and affine or 3D projection. These features share similar properties with neurons in inferior temporal cortex that are used for object recognition in primate vision. Features are efficiently detected through a staged filtering approach that identifies stable points in scale space. Image keys are created that allow for local geometric deformations by representing blurred image gradients in multiple orientation planes and at multiple scales. The keys are used as input to a nearest neighbor indexing method that identifies candidate object matches. Final verification of each match is achieved by finding a low residual least squares solution for the unknown model parameters. Experimental results show that robust object recognition can be achieved in cluttered partially occluded images with a computation time of under 2 seconds.
15,597 citations
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TL;DR: A novel scale- and rotation-invariant interest point detector and descriptor, coined SURF (Speeded Up Robust Features), which approximates or even outperforms previously proposed schemes with respect to repeatability, distinctiveness, and robustness, yet can be computed and compared much faster.
Abstract: In this paper, we present a novel scale- and rotation-invariant interest point detector and descriptor, coined SURF (Speeded Up Robust Features). It approximates or even outperforms previously proposed schemes with respect to repeatability, distinctiveness, and robustness, yet can be computed and compared much faster.
This is achieved by relying on integral images for image convolutions; by building on the strengths of the leading existing detectors and descriptors (in casu, using a Hessian matrix-based measure for the detector, and a distribution-based descriptor); and by simplifying these methods to the essential. This leads to a combination of novel detection, description, and matching steps. The paper presents experimental results on a standard evaluation set, as well as on imagery obtained in the context of a real-life object recognition application. Both show SURF's strong performance.
12,404 citations
"Annotating Dance Posture Images Usi..." refers background in this paper
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TL;DR: A novel scale- and rotation-invariant detector and descriptor, coined SURF (Speeded-Up Robust Features), which approximates or even outperforms previously proposed schemes with respect to repeatability, distinctiveness, and robustness, yet can be computed and compared much faster.
Abstract: This article presents a novel scale- and rotation-invariant detector and descriptor, coined SURF (Speeded-Up Robust Features). SURF approximates or even outperforms previously proposed schemes with respect to repeatability, distinctiveness, and robustness, yet can be computed and compared much faster. This is achieved by relying on integral images for image convolutions; by building on the strengths of the leading existing detectors and descriptors (specifically, using a Hessian matrix-based measure for the detector, and a distribution-based descriptor); and by simplifying these methods to the essential. This leads to a combination of novel detection, description, and matching steps. The paper encompasses a detailed description of the detector and descriptor and then explores the effects of the most important parameters. We conclude the article with SURF's application to two challenging, yet converse goals: camera calibration as a special case of image registration, and object recognition. Our experiments underline SURF's usefulness in a broad range of topics in computer vision.
11,276 citations
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TL;DR: Comparisons with other multiresolution texture features using the Brodatz texture database indicate that the Gabor features provide the best pattern retrieval accuracy.
Abstract: Image content based retrieval is emerging as an important research area with application to digital libraries and multimedia databases. The focus of this paper is on the image processing aspects and in particular using texture information for browsing and retrieval of large image data. We propose the use of Gabor wavelet features for texture analysis and provide a comprehensive experimental evaluation. Comparisons with other multiresolution texture features using the Brodatz texture database indicate that the Gabor features provide the best pattern retrieval accuracy. An application to browsing large air photos is illustrated.
3,895 citations
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TL;DR: This paper shows how the kernel matrix can be learned from data via semidefinite programming (SDP) techniques and leads directly to a convex method for learning the 2-norm soft margin parameter in support vector machines, solving an important open problem.
Abstract: Kernel-based learning algorithms work by embedding the data into a Euclidean space, and then searching for linear relations among the embedded data points. The embedding is performed implicitly, by specifying the inner products between each pair of points in the embedding space. This information is contained in the so-called kernel matrix, a symmetric and positive semidefinite matrix that encodes the relative positions of all points. Specifying this matrix amounts to specifying the geometry of the embedding space and inducing a notion of similarity in the input space---classical model selection problems in machine learning. In this paper we show how the kernel matrix can be learned from data via semidefinite programming (SDP) techniques. When applied to a kernel matrix associated with both training and test data this gives a powerful transductive algorithm---using the labeled part of the data one can learn an embedding also for the unlabeled part. The similarity between test points is inferred from training points and their labels. Importantly, these learning problems are convex, so we obtain a method for learning both the model class and the function without local minima. Furthermore, this approach leads directly to a convex method for learning the 2-norm soft margin parameter in support vector machines, solving an important open problem.
2,362 citations
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