Oren Barkan

Fast High Dimensional Vector Multiplication Face Recognition

This paper advances descriptor-based face recognition by suggesting a novel usage of descriptors to form an over-complete representation, and by proposing a new metric learning pipeline within the same/not-same framework. First, the Over-Complete Local Binary Patterns (OCLBP) face representation scheme is introduced as a multi-scale modified version of the Local Binary Patterns (LBP) scheme. Second, we propose an efficient matrix-vector multiplication-based recognition system. The system is based on Linear Discriminant Analysis (LDA) coupled with Within Class Covariance Normalization (WCCN). This is further extended to the unsupervised case by proposing an unsupervised variant of WCCN. Lastly, we introduce Diffusion Maps (DM) for non-linear dimensionality reduction as an alternative to the Whitened Principal Component Analysis (WPCA) method which is often used in face recognition. We evaluate the proposed framework on the LFW face recognition dataset under the restricted, unrestricted and unsupervised protocols. In all three cases we achieve very competitive results.

ITEM2VEC: Neural item embedding for collaborative filtering

Many Collaborative Filtering (CF) algorithms are item-based in the sense that they analyze item-item relations in order to produce item similarities. Recently, several works in the field of Natural Language Processing (NLP) suggested to learn a latent representation of words using neural embedding algorithms. Among them, the Skip-gram with Negative Sampling (SGNS), also known as word2vec, was shown to provide state-of-the-art results on various linguistics tasks. In this paper, we show that item-based CF can be cast in the same framework of neural word embedding. Inspired by SGNS, we describe a method we name item2vec for item-based CF that produces embedding for items in a latent space. The method is capable of inferring item-item relations even when user information is not available. We present experimental results that demonstrate the effectiveness of the item2vec method and show it is competitive with SVD.

Adaptive Compressed Tomography Sensing

One of the main challenges in Computed Tomography (CT) is how to balance between the amount of radiation the patient is exposed to during scan time and the quality of the CT image. We propose a mathematical model for adaptive CT acquisition whose goal is to reduce dosage levels while maintaining high image quality at the same time. The adaptive algorithm iterates between selective limited acquisition and improved reconstruction, with the goal of applying only the dose level required for sufficient image quality. The theoretical foundation of the algorithm is nonlinear Ridgelet approximation and a discrete form of Ridgelet analysis is used to compute the selective acquisition steps that best capture the image edges. We show experimental results where for the same number of line projections, the adaptive model produces higher image quality, when compared with standard limited angle, non-adaptive acquisition algorithms.

Gaussian process regression for out-of-sample extension

Manifold learning methods are useful for high dimensional data analysis. Many of the existing methods produce a low dimensional representation that attempts to describe the intrinsic geometric structure of the original data. Typically, this process is computationally expensive and the produced embedding is limited to the training data. In many real life scenarios, the ability to produce embedding of unseen samples is essential. In this paper we propose a Bayesian non-parametric approach for out-of-sample extension. The method is based on Gaussian Process Regression and independent of the manifold learning algorithm. Additionally, the method naturally provides a measure for the degree of abnormality for a newly arrived data point that did not participate in the training process. We derive the mathematical connection between the proposed method and the Nystrom extension and show that the latter is a special case of the former. We present extensive experimental results that demonstrate the performance of the proposed method and compare it to other existing out-of-sample extension methods.

A Mathematical Model for Extremely Low Dose Adaptive Computed Tomography Acquisition

One of the main challenges in Computed Tomography is to balance the amount of radiation exposure to the patient at the time of the scan with high image quality. We propose a mathematical model for adaptive Computed Tomography acquisition whose goal is to reduce dosage levels while maintaining high image quality at the same time. The adaptive algorithm iterates between selective limited acquisition and improved reconstruction, with the goal of applying only the dose level needed for sufficient image quality. The theoretical foundation of the algorithm is nonlinear Ridgelet approximation and a discrete form of Ridgelet analysis is used to compute the selective acquisition steps that best capture the image edges. We show experimental results where the adaptive model produces significantly higher image quality, when compared with known non-adaptive acquisition algorithms, for the same number of projection lines.

Robust mixture models for anomaly detection

We propose robust density estimation in a low dimensional space for anomaly detection. The outline of the method is as follows: first a low dimensional representation of the original data is learnt. Then, a robust density mixture model is estimated in the learnt space. Finally, the likelihood of a data point given the model parameters is used to apply anomaly detection. An efficient way for adapting the model parameters when the data distribution is changing with time is proposed. We further show how to identify the actual parameters in the original feature space that accounts for the occurrence of the anomaly. We present experimental results that demonstrate the effectiveness of the proposed methods.

A Mathematical Model for Adaptive Computed Tomography Sensing

One of the main challenges in computed tomography (CT) is how to balance between the amount of radiation the patient is exposed to during scan time and the quality of the reconstructed CT image. We propose a mathematical model for adaptive CT sensing whose goal is to reduce dosage levels while maintaining high image quality at the same time. The adaptive algorithm iterates between selective limited sensing and improved reconstruction, with the goal of applying only the dose level required for sufficient image quality. The theoretical foundation of the algorithm is nonlinear Ridgelet approximation and a discrete form of Ridgelet analysis is used to compute the selective acquisition steps that best capture the image edges. We show experimental results where for the same number of line projections, the adaptive model produces higher image quality, when compared with standard limited angle, nonadaptive sensing algorithms.