Alireza Ghasemi

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Active learning has been a popular area of research in recent years. It can be used to improve the performance of learning tasks by asking the labels of unlabeled data from the user. In these methods, the goal is to achieve the highest possible accuracy gain while posing minimum queries to the user. The existing approaches for active learning have been(More)
In this paper, we propose a novel human computation game for sentiment analysis. Our game aims at annotating sentiments of a collection of text documents and simultaneously constructing a highly discriminative lexicon of positive and negative phrases. Human computation games have been widely used in recent years to acquire human knowledge and use it to(More)
During recent years, active learning has evolved into a popular paradigm for utilizing user's feedback to improve accuracy of learning algorithms. Active learning works by selecting the most informative sample among unlabeled data and querying the label of that point from user. Many different methods such as uncertainty sampling and minimum risk sampling(More)
We propose a scale-invariant feature descriptor for representation of light-field images. The proposed descriptor can significantly improve tasks such as object recognition and tracking on images taken with recently popularized light field cameras. We test our proposed representation using various light field images of different types, both synthetic and(More)
In this paper, we address the problem of data description using a Bayesian framework. The goal of data description is to draw a boundary around objects of a certain class of interest to discriminate that class from the rest of the feature space. Data description is also known as one-class learning and has a wide range of applications. The proposed approach(More)
This paper introduces a model for finding the optimal replacement policy for Condition Based Maintenance (CBM) of a system when the information obtained from the gathered data does not reveal the system's exact degradation state, and the process of collecting data is costly or non-costly. The proposed model uses the Proportional Hazards Model (PHM)(More)
We present LCAV-31, a multi-view object recognition dataset designed specifically for benchmarking light field image analysis tasks. The principal distinctive factor of LCAV-31 compared to similar datasets is its design goals and availability of novel visual information for more accurate recognition (i.e. light field information). The dataset is composed of(More)
We propose a novel approach for detecting printed photos from natural scenes using a light-field camera. Our approach exploits the extra information captured by a light-field camera and the multiple views of scene in order to infer a compact feature vector from the variance in the distribution of the depth of the scene. We then use this feature for robust(More)