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This paper extends the idea of Universum learning to regression problems. We propose new Universum-SVM formulation for regression problems that incorporates a priori knowledge in the form of additional data samples. These additional data samples, or Universum samples, belong to the same application domain as the training samples, but they follow a different distribution. Several empirical comparisons...
Video Analytics on low/high resolution security camera images has received a considerable interest in recent years. Traffic density estimation from traffic camera images can be considered as one of these subjects. Traditionally GPS data from commercial vehicle fleets have been utilized to estimate traffic density on roads. Traffic density estimation has been implemented using image processing and...
Indoor localization becomes a research focus in recent years since. Smartphone-based pedestrian dead reckoning (PDR) is one of the widely-adopted localization techniques with limiting problems such as the drift of inertial sensors. Bluetooth Low Energy (BLE) has better performance result which makes it an auxiliary tool for PDR to correct errors. But BLE fingerprint sampling and calibrating are time-consuming...
In computerized detection of clustered microcalcifications (MCs) from mammogram images the occurrence of false positives (FPs) varies greatly from case to case. In this work, we develop a probabilistic modeling approach to estimate the number of individual FPs present in a detected MC lesion. We describe the number of true positives (TPs) by a Poisson-Binomial probability distribution, wherein a logistic...
Traditional data stream classification techniques assume that the stream of data is generated from a single non-stationary process. On the contrary, a recently introduced problem setting, referred to as Multistream Classification involves two independent non-stationary data generating processes. One of them is the source stream that continuously generates labeled data instances. The other one is the...
This paper deals with sound source localization and number estimation in indoor environments using a circular microphone array. Multiple sound source localization is achieved by performing single source localization at each selected time-frequency (TF) point of received signals after short-time Fourier transform. A TF point selection method is proposed to reduce the computational time, which depends...
Multi-pitch analysis of polyphonic music requires estimating concurrent pitches (estimation) and organizing them into temporal streams according to their sound sources (streaming). This is challenging for approaches based on audio alone due to the polyphonic nature of the audio signals. Video of the performance, when available, can be useful to alleviate some of the difficulties. In this paper, we...
Natural and affective handshakes of two participants define the course of dyadic interaction. Affective states of the participants are expected to be correlated with the nature of the dyadic interaction. In this paper, we extract two classes of the dyadic interaction based on temporal clustering of affective states. We use the k-means temporal clustering to define the interaction classes, and utilize...
Region of interest (ROI) generation is an important step in stereo-based pedestrian detection systems. In this paper, we propose an ROI generation method by fusing the color and depth information obtained from a stereo camera mounted on a vehicle. In our proposed method, a feature-based method which uses contour properties of the image is used to find the ROIs. In our feature-based ROI extraction...
Egocentric, or first-person vision which became popular in recent years with an emerge in wearable technology, is different than exocentric (third-person) vision in some distinguishable ways, one of which being that the camera wearer is generally not visible in the video frames. Recent work has been done on action and object recognition in egocentric videos, as well as work on biometric extraction...
Human behavior analysis based on surveillance camera is one of hot topics in security, marketing as well as computer vision and pattern recognition, and these are useful for commercial facilities such as convenience stores or book stores. In general, since surveillance camera is placed on the ceiling near store wall to monitor customer behaviors, the majority of this research utilize human model adapted...
The aim of this work is to present an automated method for the early identification of New York Heart Association (NYHA) class change in patients with heart failure using classification techniques. The proposed method consists of three main steps: a) data processing, b) feature selection, and c) classification. The estimation of the severity of heart failure in terms of NYHA class is addressed as...
In this paper we present a novel approach for 3D facial expression recognition based on a registration method. The used registration method, called the Coherent Point Drift (CPD), is applied to estimate complex non-linear and nonrigid transformation between 3D facial surfaces. The computed transformation allows to recover shape deformations that are induced by facial expression variations. Machine...
In software project management, software development effort estimation (SDEE) is one of the critical activities. Analogy-Based Estimation (ABE) is most popular estimation technique suggested in SDEE literature [1, 7, 22]. Researchers have proposed various methods to improve the accuracy of ABE by adjusting the retrieved solution. The research suggests all published calibration methods depend on linear...
In data stream analyses, detecting the concept drift accurately is important to maintain the classification performance. Most drift detection methods assume that the class labels become available immediately after a data sample arrives. However, this assumption is overly optimistic, as labeling costs are high and much time is needed to obtain the label of data samples. Therefore, it is un-realistic...
This paper discusses the effect of classification in estimating the amount of effort (in man-days) associated with code development. Estimating the effort requirements for new software projects is especially important. As outliers are harmful to the estimation, they are excluded from many estimation models. However, such outliers can be identified in practice once the projects are completed, and so...
Support vector machines (SVMs) are widely-used for classification in machine learning and data mining tasks. However, they traditionally have been applied to small to medium datasets. Recent need to scale up with data size has attracted research attention to develop new methods and implementation for SVM to perform tasks at scale. Distributed SVMs are relatively new and studied recently, but the distributed...
Gender estimation has received increased attention due to its use in a number of pertinent security and commercial applications. Automated gender estimation algorithms are mainly based on extracting representative features from face images. In this work we study gender estimation based on information deduced jointly from face and body, extracted from single-shot images. The approach addresses challenging...
In this paper, we propose a novel regression-based method for employing privileged information to estimate the height using human metrology. The actual values of the anthropometric measurements are difficult to estimate accurately using state-of-the-art computer vision algorithms. Hence, we use ratios of anthropometric measurements as features. Since many anthropometric measurements are not available...
Different types of Bayesian networks may be used for supervised classification. We combine such approaches together with feature selection and discretization and we show that such combination gives rise to powerful classifiers. A large choice of data sets from the UCI machine learning repository are used in our experiments and an application to Epilepsy type prediction based on PET scan data confirms...
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