A Probabilistic Tri-Class Support Vector Machine

Ramírez, Juan Antonio Ortega; Abril, Luis González; Morente, Francisco Velasco; Ángulo, Cecilio

doi:10.13176/11.237

Cited by 2 publications

(1 citation statement)

References 17 publications

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“…The different generated features such as 7 Hu moments, FD and count of raised finger could definitely used for complex hand sign of sign language. In this paper nearest neighborhood classification using Euclidean distance is developed for recognition but in future efficient classification and recognition algorithm such as Navies Bayesian classification [18] [19], support vector machine [20][21] [22] or Genetic algorithm [23] can be used for large class of data.…”

Section: Discussionmentioning

confidence: 99%

Vision based Real Time Hand Gesture Recognition Techniques for Human Computer Interaction

Ghotkar¹,

Kharate²

2013

IJCA

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Natural Interface with Computer using the intelligent approaches is the need of Human Computer Interaction (HCI)applications. In this paper, three techniques were proposed and experimented for interaction with Desktop/Laptop with static hand gesture. All these techniques were using real time approach with different feature descriptors such as Fourier Descriptor(FD),7 Hu moments, Convex Hull and Finger Detection. Real time Recognition efficiency was calculated with respect to recognition time for FD and 7 Hu moments. The 300 samples were trained and stored into database for recognition. For unknown user average recognition time was required 1.7 sec using FD as a feature and 4.6 sec using 7 Hu moments and recognition efficiency was achieved 96% and 98% using 7 Hu and FD respectively. In the second technique New Finger detection algorithm was developed and experimented with Hand tracking system(HTS). In this approach, system was working in dynamic background but gives better result in static background. In the third approach, 3-D Kinect camera was used where hand segmentation was achieved using depth image and finger detection were calculated using Convex Hull. In this approach hand segmentation became easier than first two techniques. With all these approaches feature extraction using 7 Hu and FD can be extend for any other HCI application including sign language recognition. Finger counting algorithm can be combined with other descriptor in complex hand sign as a feature. Currently system is working on static hand gestures, further it will be extended to dynamic hand gesture recognition for Indian sign language interpretation.

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Section: Discussionmentioning

confidence: 99%

Vision based Real Time Hand Gesture Recognition Techniques for Human Computer Interaction

Ghotkar¹,

Kharate²

2013

IJCA

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Shrinkage learning to improve SVM with hints

Oneto

Ghio

Ridella

et al. 2015

2015 International Joint Conference on Neural Networks (IJCNN)

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The Support Vector Machine (SVM) is one of the most effective and used algorithms, when targeting classification. Despite its large success, SVM is mainly afflicted by two issues: (i) some hyperparameters must be tuned in advance and are, in practice, identified through computationally intensive procedures; (ii) possible a-priori knowledge about the problem (e.g. doctor expertise in medical applications) cannot be straightforwardly exploited. In this paper, we introduce a new approach, able to cope with the two previous problems: several experiments, performed on real-world benchmarking datasets, show that our method outperforms, on average, other techniques proposed in the literature

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A Probabilistic Tri-Class Support Vector Machine

Cited by 2 publications

References 17 publications

Vision based Real Time Hand Gesture Recognition Techniques for Human Computer Interaction

Vision based Real Time Hand Gesture Recognition Techniques for Human Computer Interaction

Shrinkage learning to improve SVM with hints

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