Browsing by Subject "Classification methods"

Now showing 1 - 9 of 9

Open Access
3D human pose search using oriented cylinders
(IEEE, 2009-09-10) Pehlivan, Selen; Duygulu, Pınar
In this study, we present a representation based on a new 3D search technique for volumetric human poses which is then used to recognize actions in three dimensional video sequences. We generate a set of cylinder like 3D kernels in various sizes and orientations. These kernels are searched over 3D volumes to find high response regions. The distribution of these responses are then used to represent a 3D pose. We use the proposed representation for (i) pose retrieval using Nearest Neighbor (NN) based classification and Support Vector Machine (SVM) based classification methods, and for (ii) action recognition on a set of actions using Dynamic Time Warping (DTW) and Hidden Markov Model (HMM) based classification methods. Evaluations on IXMAS dataset supports the effectiveness of such a robust pose representation. ©2009 IEEE.
Open Access
Adaptive hierarchical space partitioning for online classification
(IEEE, 2016) Kılıç, O. Fatih; Vanlı, N. D.; Özkan, H.; Delibalta, İ.; Kozat, Süleyman Serdar
We propose an online algorithm for supervised learning with strong performance guarantees under the empirical zero-one loss. The proposed method adaptively partitions the feature space in a hierarchical manner and generates a powerful finite combination of basic models. This provides algorithm to obtain a strong classification method which enables it to create a linear piecewise classifier model that can work well under highly non-linear complex data. The introduced algorithm also have scalable computational complexity that scales linearly with dimension of the feature space, depth of the partitioning and number of processed data. Through experiments we show that the introduced algorithm outperforms the state-of-the-art ensemble techniques over various well-known machine learning data sets.
Open Access
Authorship attribution: performance of various features and classification methods
(IEEE, 2007-11) Bozkurt, İlker Nadi; Bağlıoğlu, Özgür; Uyar, Erkan
Authorship attribution is the process of determining the writer of a document. In literature, there are lots of classification techniques conducted in this process. In this paper we explore information retrieval methods such as tf-idf structure with support vector machines, parametric and nonparametric methods with supervised and unsupervised (clustering) classification techniques in authorship attribution. We performed various experiments with articles gathered from Turkish newspaper Milliyet. We performed experiments on different features extracted from these texts with different classifiers, and combined these results to improve our success rates. We identified which classifiers give satisfactory results on which feature sets. According to experiments, the success rates dramatically changes with different combinations, however the best among them are support vector classifier with bag of words, and Gaussian with function words. ©2007 IEEE.
Open Access
Developing a text categorization template for Turkish news portals
(IEEE, 2011) Toraman, Çağrı; Can, Fazlı; Koçberber, Seyit
In news portals, text category information is needed for news presentation. However, for many news stories the category information is unavailable, incorrectly assigned or too generic. This makes the text categorization a necessary tool for news portals. Automated text categorization (ATC) is a multifaceted difficult process that involves decisions regarding tuning of several parameters, term weighting, word stemming, word stopping, and feature selection. In this study we aim to find a categorization setup that will provide highly accurate results in ATC for Turkish news portals. We also examine some other aspects such as the effects of training dataset set size and robustness issues. Two Turkish test collections with different characteristics are created using Bilkent News Portal. Experiments are conducted with four classification methods: C4.5, KNN, Naive Bayes, and SVM (using polynomial and rbf kernels). Our results recommends a text categorization template for Turkish news portals and provides some future research pointers. © 2011 IEEE.
Open Access
Düşme tespiti için sınıflandırma yöntemlerinin karşılaştırılması
(IEEE, 2014-04) Çatalbaş, Bahadır; Yücesoy, Burak; Seçer, G.; Aslan, Murat
Bu bildiride giyilebilir yapıda olan ve üç boyutlu ölçüm alabilen bir ivmeölçerin çıktılarını kullanarak düşme tespiti yapan farklı algoritmaların karşılaştırılması yapılmıştır. Karşılaştırma amacıyla destek vektör makineleri, yapay sinir ağları ile elde edilen sınıflandırıcılar ve kural bazlı bir sınıflandırıcı kullanılmıştır. Sınıflandırıcıların tasarlanması ve dogrulanması amacıyla 7 farklı denekten üçer defa düşme ve düşme dışındaki günlük aktivitelere ilişkin ivmeölçer verileri toplanmıştır. Yapılan karşılaştırma sonucunda tespit doğruluğu en yüksek algoritmanın %87,76 ile destek vektör makineleri olduğu bulunmuştur. En yüksek düşme tespit oranı da %90,91 ˘ olarak kural bazlı sınıflandırıcı kullanımıyla elde edilmiştir. En yüksek özgüllük oranı %89,47 ile yine destek vektör makineleri ile elde edilmiştir.
Open Access
Human activity recognition using tag-based localization
(IEEE, 2012-04) Yurtman, Aras; Barshan, Barshan
This paper provides a comparative study on the different techniques of classifying human activities using a tag-based radio-frequency (RF) localization system. Non-uniformly-sampled data containing position measurements of the tags on the body is first converted to a uniformly-sampled one using different curve-fitting algorithms. Then, the data is partitioned into segments. Finally, various classification techniques are applied to classify human activities. Curve-fitting, segmentation, and classification methods are compared using different cross-validation techniques and the combination resulting in the best performance is presented. The results indicate that the system demonstrates acceptable performance despite the fact that tag-based RF localization is not very accurate.
Open Access
İki durumlu bir beyin bilgisayar arayüzünde özellik çıkarımı ve sınıflandırma
(IEEE, 2017-10) Altındiş, Fatih; Yılmaz, B.
Beyin bilgisayar arayüzü (BBA) teknolojisi motor nöronlarının özelliğini kaybeden ve hareket kabiliyeti kısıtlanmış ALS ve felçli hastalar gibi birçok kişinin dış dünya ile iletişimini sağlamaya yönelik kullanılmaktadır. Bu çalışmada, Avusturya’daki Graz Üniversitesi’nde alınmış EEG veri seti kullanılarak gerçek zamanlı EEG işleme simülasyonu ile motor hayal etme sınıflandırılması amaçlanmıştır. Bu veri setinde sağ el ya da sol elin hareket ettirilme hayali esnasında 8 kişiden alınmış iki kanallı EEG sinyalleri bulunmaktadır. Her katılımcıdan 60 sağ ve 60 sol olmak üzere toplamda 120 adet yaklaşık 9 saniyelik motor hayal etme deneme sinyali kayıt edilmiştir. Bu sinyaller filtrelemeye tabi tutulmuştur. Yirmi dört, 32 ve 40 elemanlı özellik vektörü bant geçiren filtreler kullanarak elde edilen göreceli güç değişim değerleridir (GGDD). Bu çalışmada, lineer diskriminant analizi (LDA), k en yakın komşular (KNN) ve destek vektör makinaları (SVM) ile sınıflandırma yapılmış, en iyi sınıflandırma performansının 24 değerli özellik vektörüyle ve LDA sınıflandırma yöntemiyle elde edildiği gösterilmiştir.
Open Access
Nearest-neighbor based metric functions for indoor scene recognition
(Academic Press, 2011) Cakir, F.; Güdükbay, Uğur; Ulusoy, Özgür
Indoor scene recognition is a challenging problem in the classical scene recognition domain due to the severe intra-class variations and inter-class similarities of man-made indoor structures. State-of-the-art scene recognition techniques such as capturing holistic representations of an image demonstrate low performance on indoor scenes. Other methods that introduce intermediate steps such as identifying objects and associating them with scenes have the handicap of successfully localizing and recognizing the objects in a highly cluttered and sophisticated environment. We propose a classification method that can handle such difficulties of the problem domain by employing a metric function based on the Nearest-Neighbor classification procedure using the bag-of-visual words scheme, the so-called codebooks. Considering the codebook construction as a Voronoi tessellation of the feature space, we have observed that, given an image, a learned weighted distance of the extracted feature vectors to the center of the Voronoi cells gives a strong indication of the image's category. Our method outperforms state-of-the-art approaches on an indoor scene recognition benchmark and achieves competitive results on a general scene dataset, using a single type of descriptor. © 2011 Elsevier Inc. All rights reserved.
Open Access
A signal representation approach for discrimination between full and empty hazelnuts
(IEEE, 2007) Onaran, İbrahim; İnce, N. F.; Tevfik, A. H.; Çetin, A. Enis
We apply a sparse signal representation approach to impact acoustic signals to discriminate between empty and full hazelnuts. The impact acoustic signals are recorded by dropping the hazelnut shells on a metal plate. The impact signal is then approximated within a given error limit by choosing codevectors from a special dictionary. This dictionary was generated from sub-dictionaries that are individually generated for the impact signals corresponding to empty and full hazelnut. The number of codevectors selected from each sub-dictionary and the approximation error within initial codevectors are used as classification features and fed to a Linear Discriminant Analysis (LDA). We also compare this algorithm with a baseline approach. This baseline approach uses features which describe the time and frequency characteristics of the given signal that were previously used for empty and full hazelnut separation. Classification accuracies of 98.3% and 96.8% were achieved by the proposed approach and base algorithm respectively. The results we obtained show that sparse signal representation strategy can be used as an alternative classification method for undeveloped hazelnut separation with higher accuracies.