Robust Subspace Detectors Based on <i>α</i>-Divergence With Application to Detection in Imaging

Rekavandi, Aref Miri; Seghouane, Abd‐Krim; Evans, Robin J.

doi:10.1109/tip.2021.3077139

Cited by 12 publications

(5 citation statements)

References 61 publications

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“…In the M step of the EM algorithm, the goal is to maximize the lower bound L with respect to β which is literally the ML of the complete data log likelihood. It is understood, however, that when ML is used with a large number of samples (N → ∞), it is equivalent to minimizing the KL divergence between the observed empirical density and the model density [17]. It is therefore possible to reformulate the relationships and replace the KL divergence with the α-divergence (that is, to transform the KL into an extended form that has valuable properties, including robustness) [18].…”

Section: Proposed Methodsmentioning

confidence: 99%

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Extended Expectation Maximization for Under-Fitted Models

Rekavandi

Seghouane

Boussaid

et al. 2023

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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In this paper, we generalize the well-known Expectation Maximization (EM) algorithm using the α−divergence for Gaussian Mixture Model (GMM). This approach is used in robust subspace detection when the number of parameters is kept small to avoid overfitting and large estimation variances. The level of robustness can be tuned by the parameter α. When α → 1, our method is equivalent to the standard EM approach and for α < 1 the method is robust against potential outliers. Simulation results show that the method outperforms the standard EM when it comes to mismatches between noise models and their realizations. In addition, we use the proposed method to detect active brain areas using collected functional Magnetic Resonance Imaging (fMRI) data during task-related experiments.

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Section: Proposed Methodsmentioning

confidence: 99%

“…For α → 1 we have w nk → γ nk , and the proposed method becomes the standard EM algorithm. In the case of K = 1, we obtain the standard form of the weighted least square estimator in Gaussian nominal noise [17]. With the same approach, one can estimate other unknown parameters as…”

Section: Proposed Methodsmentioning

confidence: 99%

Extended Expectation Maximization for Under-Fitted Models

Rekavandi

Seghouane

Boussaid

et al. 2023

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Self Cite

View full text Add to dashboard Cite

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“…2 ), Kumbhakar [206] for modelling the streamwise velocity profile in open-channel flows, Sigmon et al [335] for the improvement of genetic quality control in mouse research for biomedical applications (with γ = 2), Zhang et al [420] for the design of a noise-adaptation adapted generative adversarial network for medical image analysis (with γ = 1 2 ), Chen et al [79] for clustering high-dimensional microbial data from RNA sequencing (with γ = 1 2 ), Dharmawan et al [114] for the development of improvements in long-term cell observations via semiconductor-chips-based lensless holographic microscopy, Liu & Sun [229] for analyzing approximate inferences in Bayesian neural networks, Rekavandi et al [307] for detections in functional magnetic resonance imaging (fMRI) as well as hyperspectral and synthetic aperture radar (SAR) data, Seghouane & Shokouhi [325] for adaptive learning within robust radial basis function networks (RBFN), and Wang et al [388] for recommender-system relevant collaborative filtering in sparse data.…”

Section: ) Construction Principle For the Estimation Of The Minimum D...mentioning

confidence: 99%

A precise bare simulation approach to the minimization of some distances. Foundations

Broniatowski¹,

Stummer²

2021

Preprint

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In information theory -as well as in the adjacent fields of statistics, machine learning, artificial intelligence, signal processing and pattern recognition -many flexibilizations of the omnipresent Kullback-Leibler information distance (relative entropy) and of the closely related Shannon entropy have become frequently used tools. To tackle corresponding constrained minimization (respectively maximization) problems by a newly developed dimension-free bare (pure) simulation method, is the main goal of this paper. Almost no assumptions (like convexity) on the set of constraints are needed, within our discrete setup of arbitrary dimension, and our method is precise (i.e., converges in the limit). As a side effect, we also derive an innovative way of constructing new useful distances/divergences. To illustrate the core of our approach, we present numerous examples. The potential for widespread applicability is indicated, too; in particular, we deliver many recent references for uses of the involved distances/divergences and entropies in various different research fields (which may also serve as an interdisciplinary interface).

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“…O BJECT detection is at the heart of many computer vision applications and has grown in importance over the last decade. It plays a crucial role in modern computer vision tasks such as autonomous driving [1], [2], pedestrian identification [3], [4], image captioning [5], [6], object tracking [7], [8], ship detection [9], [10] face recognition [11], [12], traffic control [13], [14], animal detection [15], [16], action recognition [17], [18], environment surveillance [19], [20], video checking in sports [21], [22], and many others. Object detection methods have become increasingly popular with the advances in deep learning and GPU power that allow Deep Neural Nets (DNNs) to be trained faster and more efficiently in recent years.…”

Section: Introductionmentioning

confidence: 99%

A Guide to Image and Video based Small Object Detection using Deep Learning : Case Study of Maritime Surveillance

Rekavandi¹,

Liu²,

Boussaid³

et al. 2022

Preprint

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Small object detection (SOD) in optical images and videos is a challenging problem that even state-of-the-art generic object detection methods fail to accurately localize and identify such objects. Typically, small objects appear in real-world due to large camera-object distance. Because small objects occupy only a small area in the input image (e.g., less than 10%), the information extracted from such a small area is not always rich enough to support decision making. Multidisciplinary strategies are being developed by researchers working at the interface of deep learning and computer vision to enhance the performance of SOD deep learning based methods. In this paper, we provide a comprehensive review of over 160 research papers published between 2017 and 2022 in order to survey this growing subject. This paper summarizes the existing literature and provide a taxonomy that illustrates the broad picture of current research. We investigate how to improve the performance of small object detection in maritime environments, where increasing performance is critical. By establishing a connection between generic and maritime SOD research, future directions have been identified. In addition, the popular datasets that have been used for SOD for generic and maritime applications are discussed, and also well-known evaluation metrics for the state-of-the-art methods on some of the datasets are provided.

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Robust Subspace Detectors Based on α-Divergence With Application to Detection in Imaging

Cited by 12 publications

References 61 publications

Extended Expectation Maximization for Under-Fitted Models

Extended Expectation Maximization for Under-Fitted Models

A precise bare simulation approach to the minimization of some distances. Foundations

A Guide to Image and Video based Small Object Detection using Deep Learning : Case Study of Maritime Surveillance

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