Browsing by Subject "K-nearest neighbors"

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    Classification of leg motions by processing gyroscope signals
    (IEEE, 2009) Tunçel, Orkun; Altun, Kerem; Barshan, Billur
    In this study, eight different leg motions are classified using two single-axis gyroscopes mounted on the right leg of a subject with the help of several pattern recognition techniques. The methods of least squares, Bayesian decision, k-nearest neighbor, dynamic time warping, artificial neural networks and support vector machines are used for classification and their performances are compared. This study comprises the preliminary work for our future studies on motion recognition with a much wider scope.
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    Classifying human leg motions with uniaxial piezoelectric gyroscopes
    (2009) Tunçel O.; Altun, K.; Barshan, B.
    This paper provides a comparative study on the different techniques of classifying human leg motions that are performed using two low-cost uniaxial piezoelectric gyroscopes worn on the leg. A number of feature sets, extracted from the raw inertial sensor data in different ways, are used in the classification process. The classification techniques implemented and compared in this study are: Bayesian decision making (BDM), a rule-based algorithm (RBA) or decision tree, least-squares method (LSM), k-nearest neighbor algorithm (k-NN), dynamic time warping (DTW), support vector machines (SVM), and artificial neural networks (ANN). A performance comparison of these classification techniques is provided in terms of their correct differentiation rates, confusion matrices, computational cost, and training and storage requirements. Three different cross-validation techniques are employed to validate the classifiers. The results indicate that BDM, in general, results in the highest correct classification rate with relatively small computational cost. © 2009 by the authors.
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    MUCKE participation at retrieving diverse social images task of MediaEval 2013
    (CEUR-WS, 2013) Armağan, Anıl; Popescu, A.; Duygulu, Pınar
    The Mediaeval 2013 Retrieving Diverse Social Image Task addresses the challenge of improving both relevance and diversity of photos in a retrieval task on Flickr. We propose a clustering based technique that exploits both textual and visual information. We introduce a k-Nearest Neighbor (k-NN) inspired re-ranking algorithm that is applied before clustering to clean the dataset. After the clustering step, we exploit social cues to rank clusters by social relevance. From those ranked clusters images are retrieved according to their distance to cluster centroids.