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Labeled data, in real world, is quite scarce compared with unlabeled data. Manual annotation is usually expensive and inefficient. Active learning paradigm is used to handle this problem by identifying the most informative instances to annotate. In this paper, we proposed a new active learning algorithm based on nonparallel support vector machine. Numeric experiment shows the effective performance...
Humans interact with each other using different communication modalities including speech, gestures and written documents. In the absence of one modality or presence of a noisy modality, other modalities can benefit precision of systems. HCI systems can also benefit from these multimodal communication models for different machine learning tasks. The provision of multiple modalities is motivated by...
Activity Recognition is important in order to facilitate elderly residents' and their caregivers' needs. This problem has been widely investigated using different methods including probabilistic and Markovian approaches. The focus of this paper is to perform activity recognition more accurately than existing approaches using non-intrusive sensors. We represent motion sensors of smart environments...
The paper presents Echo State Network (ESN) as classifier to diagnose the abnormalities in mammogram images. Abnormalities in mammograms can be of different types. An efficient system which can handle these abnormalities and draw correct diagnosis is vital. We experimented with wavelet and Local Energy based Shape Histogram (LESH) features combined with Echo State Network classifier. The suggested...
In this preliminary research we examine the suitability of hierarchical strategies of multi-class support vector machines for classification of induced pluripotent stem cell (iPSC) colony images. The iPSC technology gives incredible possibilities for safe and patient specific drug therapy without any ethical problems. However, growing of iPSCs is a sensitive process and abnormalities may occur during...
Information extraction from signals has been a long time research topic and numerous signal transforms have been defined for the same purpose. In this paper, a comparative study of various signal transforms on basis of time-frequency (TF) resolution, cross-terms suppression and maximum information content has been presented. The transforms considered for the analysis are Frequency domain transforms,...
Motor imagery (MI) based electroencephalogram (EEG) signals are a widely used form of input in brain computer interface systems (BCIs). Although there are a number of ways to classify data, a question still persists as to which technique should be employed in the domain of MI based EEG signals. In this paper, an attempt is made to find the best classification algorithm and feature extraction technique...
In this paper, we adapt two existing methods to perform semi-supervised temporal clustering: Aligned Cluster Analysis (ACA), a temporal clustering algorithm, and Constrained Spectral Clustering, a semi-supervised clustering algorithm. In the first method, we add side information in the form of pair wise constraints to its objective function, and in the second, we add a temporal search to its framework...
In this paper, we propose classifiers based on Tensor Voting (TV) framework for supervised binary and multiclass problems. Traditional classification approaches classify a test sample or point based on its proximity to classes of a training set, where proximity is generally taken as some variant of the Euclidean distance in the original or some transformed higher dimensional space. However, we may...
Advances in computer technology have enabled the collection, digitization, and automated processing of huge archives of bioacoustic sound. Many of the tools previously used in bioacoustics research work well with small to medium-sized audio collections, but are challenged when processing large collections ranging from tens of terabytes to petabyte size. The Orchive is a system that assists researchers...
Sign Language is the language of gestures used by mute people for non-verbal communication. This paper is about a novel approach for the recognition of number signs of Pakistan Sign Language. An image sign is captured from running video when signer nods head. Deep pixels-based analysis of the scanned image is pursued for the recognition of fingers (from index to small finger) while thumb's position...
Despite the advent and popularity of low-cost commercial sensors (e.g., Microsoft Kinect), research in 3D vision still primarily focuses on the development of advanced algorithms geared towards high resolution data. This paper presents a comparative performance evaluation of renowned state-of-the-art 3D local surface descriptors for the task of registration of both high and low resolution range image...
In embedded systems, many numerical algorithms are implemented with fixed-point arithmetic to meet area cost and power constraints. Fixed-point encoding decisions can significantly affect cost and performance. To evaluate their impact on accuracy, designers resort to simulations. Their high running-time prevents thorough exploration of the design-space. To address this issue, analytical modeling techniques...
Parkinson's disease is a complex condition currently monitored at home with paper diaries which rely on subjective and unreliable assessment of motor function at nonstandard time intervals. We present an innovative wearable and unobtrusive monitoring system for patients which can help provide physicians with significantly improved assessment of patients' responses to drug therapies and lead to better-targeted...
The high spectral and spatial resolution of hyperspectral images increases the capability to distinguish physical materials and objects, presenting new challenges to image analysis and classification. In fact, many studies have been conducted to extract and integrate spectral and contextual information in the classification process. However, the availability of various spatial features (e.g. morphological...
This paper presents a comparative study of Support Vector Machines (SVMs) which is classified based on melanoma imaging technique. After the preprocessing and segmentation of a set of distinct 35 images, the extracted features were Asymmetry, Border, Color, Diameter,(ABCD) Entropy and Correlations respectively. Further the resultant data was fed into five different SVM classifiers namely linear, poly,...
Local appearance descriptors are widely used on facial emotion recognition tasks. With these descriptors, image filters, such as Gabor wavelet or local binary patterns (LBP) are applied on the whole or specific regions of the face to extract facial appearance changes. But it is also clear that beside feature descriptor; choice of suitable learning method that integrates feature novelty is vital. The...
Good performance of pedestrian detection in an automatic driving system is a necessary task. Many pedestrian detection algorithm use Histogram Oriented Gradient (HOG) for feature extraction and Support Vector Machine (SVM) for classification. Some papers use additional features with HOG, such as Local Binary Pattern (HOG-LBP), to improve the performance. Neural Network and Extreme Learning Machine...
The objective of this paper lies on the characterization of osteoporosis disease using fractal analysis of X-Ray images. The method consists of a pre-processing step followed by a feature extraction based on the fractional Brownian motion (fBm) model. The Support Vector Machine (SVM) was used as a classifier to distinguish between two populations composed of Osteoporotic Patients (OP) and Control...
In many applications such as dealing with database, continuous environment and humanoid robots, the machine often deals with large amount of data every day of work. Dealing with large amount of data requires fast as well as accurate learning algorithms to do the classification. A new supervised non parametric Partial Histogram Bayes learning algorithm (PHBayes) is proposed and presented in this paper...
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