Conference Paper

Pattern Classiffication using SVM with GMM Data Selection Training Methode

Electr. Eng. Dept., Amirkabir Univ. of Technol., Tehran, Iran
DOI: 10.1109/ICSPC.2007.4728496 Conference: Signal Processing and Communications, 2007. ICSPC 2007. IEEE International Conference on
Source: IEEE Xplore

ABSTRACT In pattern recognition, support vector machines (SVM) as a discriminative classifier and Gaussian mixture model as a generative model classifier are two most popular techniques. Current state-of-the-art systems try to combine them together for achieving more power of classification and improving the performance of the recognition systems. Most of recent works focus on probabilistic SVM/GMM hybrid methods but this paper presents a novel method for SVM/GMM hybrid pattern classification based on training data selection. This system uses the output of the Gaussian mixture model to choose training data for SVM classifier. Results on databases are provided to demonstrate the effectiveness of this system. We are able to achieve better error-rates that are better than the current systems.

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    ABSTRACT: In this paper we explain a fully automatic system for airplane detection and tracking based on wavelet transform and Support Vector Machine (SVM). By using 50 airplane images in different situations, models are developed to recognize airplane in the first frame of a video sequence. To train a SVM classifier for classifying pixels belong to objects and background pixels, vectors of features are built. The learned model can be used to detect the airplane in the original video and in the novel images. For original video, the system can be considered as a generalized tracker and for novel images it can be interpreted as method for learning models for object recognition. After airplane detection in the first frame, the feature vectors of this frame are used to train the SVM classifier. For new video frame, SVM is applied to test the pixels and form a confidence map. The 4th level of Daubechies's wavelet coefficients corresponding to input image are used as features. Conducting simulations, it is demonstrated that airplane detection and tracking based on wavelet transform and SVM classification result in acceptable and efficient performance. The experimental results agree with the theoretical results.
    System Theory, 2009. SSST 2009. 41st Southeastern Symposium on; 04/2009


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