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Photo privacy detection based on text classification and face clustering 6

Photo privacy detection based on text classification and fac...

6th International Conference Information Technology and Nanotechnology. Session Image Processing and Earth Remote Sensing, ITNT-IPERS 2020

作者： Kopeykina, Lyudmila Savchenko, Andrey National Research University Higher School of Economics Nizhny Novgorod Russia Laboratory of Algorithms and Technologies for Network Analysis National Research University Higher School of Economics Nizhny Novgorod Russia

Nowadays, the photo privacy detection is becoming an acute task due to a wide spread of mobile devices with photos published on social networks. As a photo might contain private or sensitive data, there is an urgent need to accurately determine them and impose restrictions on their processing. In this paper we focus on the task of personal data detection in a photo gallery. A novel two-stage approach is proposed. At first, text of scanned documents is processed based on an EAST text detector, and extracted text is recognized using Tesseract and neural network classifier. At the second stage, face clustering is implemented for the remaining photos to identify large groups of people (friends, relatives) whose photos also refer to personal data and must be processed directly on a mobile device. The remaining images can be sent to a remote server for processing with higher accuracy. The experimental results of text recognition and face clustering methods using various convolutional networks for facial features extraction are presented. Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0)

关键词： Classification (of information)

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Improving the Accuracy of One-Shot Detectors for Small Objects in X-ray Images

Improving the Accuracy of One-Shot Detectors for Small Objec...

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2020 International Russian Automation Conference, RusAutoCon 2020

作者： Demochkina, Polina Savchenko, Andrey V. National Research University Higher School of Economics Nizhny Novgorod Russia National Research University Higher School of Economics Laboratory of Algorithms and Technologies for Network Analysis Nizhny Novgorod Russia

ISBN: (纸本)9781728161303

In this paper, we address the problem of detecting small objects on high-quality X-ray imagesusing deep neural networks. We propose to implement the two-stage approach, in which, firstly, input image issplit into partially overlapping blocks to make small objects more discriminative for detection. Secondly, the small blocks are fed into conventional single-shot detectors. These detectors are trained using the blocks of the training images extracted by the same *** datasets of X-ray images from the customs inspection complex are examined in the experimental study. It was shown thatthe proposed algorithm with data augmentationleads tomore precise results when compared to the conventional technique:ourmethod outperforms the traditional approach by 5.4 - 25.7% depending on the type of used backbone convolutional neural network. © 2020 IEEE.

关键词： Deep neural networks

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A New Sport Teams Logo Dataset for Detection Tasks 1

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International Conference on Computer Vision and Graphics, ICCVG 2020

作者： Kuznetsov, Andrey Savchenko, Andrey V. St. Petersburg Department of Steklov Institute of Mathematics St. Petersburg Russia Laboratory of Algorithms and Technologies for Network Analysis National Research University Higher School of Economics Nizhny Novgorod Russia

ISBN: (数字)9783030590062

ISBN: (纸本)9783030590055

In this research we introduce a new labelled SportLogo dataset, that contains images of two kinds of sports: hockey (NHL) and basketball (NBA). This dataset presents several challenges typical for logo detection tasks. A huge number of occlusions and logo view changes during playing games lead to an ambiguity of a straightforward detection approach use. Another issue is logo style changes due to seasonal kits updates. In this paper we propose a two stage approach, in which, firstly, an input image is processed by a specially trained scene recognition convolutional neural network. Second, conventional object detectors are applied only for sport scenes. Experimental study contains results of different combinations of backbone and detector convolutional neural networks. It was shown that MobileNet + YOLO v3 solution provides the best quality results on the designed dataset (mAP = 0.74, Recall = 0.87). © 2020, Springer Nature Switzerland AG.

关键词： Sports

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Detection and recognition of food in photo galleries for analysis of user preferences 17th

Detection and recognition of food in photo galleries for an...

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17th International Conference on Image analysis and Recognition, ICIAR 2020

作者： Miasnikov, Evgeniy Savchenko, Andrey St. Petersburg Department of Steklov Institute of Mathematics St. Petersburg Russia Laboratory of Algorithms and Technologies for Network Analysis National Research University Higher School of Economics Nizhny Novgorod Russia

ISBN: (纸本)9783030503468

Food analysis is one of the most important parts of user preference prediction engines for recommendation systems in the travel domain. In this paper, we describe and study the neural network method that allows you to recognize food in a gallery of photos taken with mobile devices. The described method consists of three main stages, including the classification of scenes, food detection, and subsequent classification. An essential feature of the developed method is the use of lightweight neural network models, which allows its usage on mobile devices. The development of the method was carried out using both known open data and a proprietary data set. © Springer Nature Switzerland AG 2020.

关键词： Convolutional neural networks

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Faster algorithms for Sparse ILP and Hypergraph Multi-Packing/Multi-Cover Problems

arXiv

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arXiv 2022年

作者： Gribanov, Dmitry Malyshev, Dmitry Zolotykh, Nikolai Shumilov, Ivan Laboratory of Algorithms and Technologies for Network Analysis HSE University 136 Rodionova Ulitsa Nizhny Novgorod603093 Russia Lobachevsky State University of Nizhny Novgorod 23 Gagarina Avenue Nizhny Novgorod603950 Russia Mathematics of Future Technologies Center Lobachevsky State University of Nizhni Novgorod 23 Gagarina Avenue Nizhny Novgorod603950 Russia

In our paper, we consider the following general problems: check feasibility, count the number of feasible solutions, find an optimal solution, and count the number of optimal solutions in P ∩ Zn, assuming that P is a polyhedron, defined by systems Ax ≤ b or Ax = b, x ≥ 0 with a sparse matrix A. We develop algorithms for these problems that outperform state of the art ILP and counting algorithms on sparse instances with bounded elements. We use known and new methods to develop new exponential algorithms for Edge/Vertex Multi-Packing/Multi-Cover Problems on graphs and hypergraphs. This framework consists of many different problems, such as the Stable Multi-set, Vertex Multi-cover, Dominating Multi-set, Set Multi-cover, Multi-set Multi-cover, and Hypergraph Multi-matching problems, which are natural generalizations of the standard Stable Set, Vertex Cover, Dominating Set, Set Cover, and Maximum Matching problems. Copyright © 2022, The Authors. All rights reserved.

关键词： Optimal systems

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A New and Faster Representation for Counting Integer Points in Parametric Polyhedra

arXiv

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arXiv 2023年

作者： Gribanov, Dmitry V. Malyshev, Dmitry S. Pardalos, Panos M. Zolotykh, Nikolai Yu. Laboratory of Discrete and Combinatorial Optimization Moscow Institute of Physics and Technology Institutsky Lane 9 Moscow Region Dolgoprudny141700 Russia Laboratory of Algorithms and Technologies for Network Analysis HSE University 136 Rodionova Ulitsa Nizhny Novgorod603093 Russia Department of Industrial and Systems Engineering University of Florida 401 Weil Hall GainesvilleFL116595 United States Lobachevsky State University of Nizhny Novgorod 23 Gagarina Avenue Nizhny Novgorod603950 Russia

In this paper, we consider the counting function EP(y) = | Py ∩ Znx | for a parametric polyhedron Py = {x ∈ Rnx : Ax ≤ b + By}, where y ∈ Rny. We give a new representation of EP(y), called a piece-wise step-polynomial with periodic coefficients, which is a generalization of piece-wise step-polynomials and integer/rational Ehrhart’s quasi-polynomials. It gives the fastest way to calculate EP(y) in certain scenarios. The most important cases are the following: 1) We show that, for the parametric polyhedron Py defined by a standard-form system Ax = y, x ≥ 0 with a fixed number of equalities, the function EP(y) can be represented by a polynomial-time computable function. In turn, such a representation of EP(y) can be constructed by an poly(n, A∞)-time algorithm;2) Assuming again that the number of equalities is fixed, we show that integer/rational Ehrhart’s quasi-polynomials of a polytope can be computed by FPT-algorithms, parameterized by sub-determinants of A or its elements;3) Our representation of EP is more efficient than other known approaches, if A has bounded elements, especially if it is sparse in addition;Additionally, we provide a discussion about possible applications in the area of compiler optimization. In some "natural" assumptions on a program code, our approach has the fastest complexity bounds. Copyright © 2023, The Authors. All rights reserved.

关键词： Polynomial approximation

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Dota Underlords Game Is NP-Complete

arXiv

引用

arXiv 2020年

作者： Ponomarenko, Alexander A. Sirotkin, Dmitry V. Laboratory of Algorithms and Technologies for Network Analysis National Research University Higher School of Economics Nizhny Novgorod Russia

In this paper, we demonstrate how the problem of the optimal team choice in the popular computer game Dota Underlords can be reduced to the problem of linear integer programming. We propose a model and solve it for the real data. We also prove that this problem belongs to the NP-complete class and show that it reduces to the maximum edge weighted clique problem. Copyright © 2020, The Authors. All rights reserved.

关键词： Computer games

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Optimization of Gain in Symmetrized Itakura-Saito Discrimination for Pronunciation Learning 19th

Optimization of Gain in Symmetrized Itakura-Saito Discrimina...

引用

19th International Conference on Mathematical Optimization Theory and Operations Research, MOTOR 2020

作者： Savchenko, Andrey V. Savchenko, Vladimir V. Savchenko, Lyudmila V. Laboratory of Algorithms and Technologies for Network Analysis National Research University Higher School of Economics Nizhny Novgorod Russia Nizhny Novgorod State Linguistic University Nizhny Novgorod Russia Department of Information Systems and Technologies National Research University Higher School of Economics Nizhny Novgorod Russia

ISBN: (纸本)9783030499877

This paper considers an assessment and evaluation of the pronunciation quality in computer-aided language learning systems. We propose the novel distortion measure for speech processing by using the gain optimization of the symmetrized Itakura-Saito divergence. This dissimilarity is implemented in a complete algorithm for pronunciation learning and improvement. At its first stage, a user has to achieve a stable pronunciation of all sounds by matching them with sounds of an ideal speaker. At the second stage, the recognition of sounds and their short sequences is carried out to guarantee the distinguishability of learned sounds. The training set may contain not only ideal sounds but the best utterances of a user obtained at the previous step. Finally, the word recognition accuracy is estimated by using deep neural networks fine-tuned on the best words from a user. Experimental study shows that the proposed procedure makes it possible to achieve high efficiency for learning of sounds and their sequences even in the presence of noise in an observed utterance. © 2020, Springer Nature Switzerland AG.

关键词： Convolutional neural networks

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Automatic Privacy Detection in Scanned Document Images Based on Deep Neural networks

Automatic Privacy Detection in Scanned Document Images Based...

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2019 International Russian Automation Conference, RusAutoCon 2019

作者： Kopeykina, Lyudmila Savchenko, Andrey V. National Research University Higher School of Economics Nizhny Novgorod Russia Laboratory of Algorithms and Technologies for Network Analysis National Research University Higher School of Economics Nizhny Novgorod Russia

ISBN: (纸本)9781728102658

The authors consider the problem of automatic detection of private scanned documents based on text recognition with deep neural networks. The paper suggests implementing a two-phase approach with the first stage which includes efficient EAST text detection and recognition using Tesseract OCR Engine. Secondly, the authors classify the privacy of a scanned document by deep neural networks applied to the extracted text. After that, a special dataset is gathered in order to train these networks. The experiments show that using OCR Engine for both text detection and segmentation ends up with relatively poor identification of private documents when compared to preliminary text detection with EAST method. Moreover, conventional keyword spotting using the list of sensitive words is less accurate when compared to neural network-based methods. Finally, it was demonstrated that the classification of a bag of most frequent words outperforms traditional text classification techniques with LSTM and convolutional networks. © 2019 IEEE.

关键词： Deep neural networks

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Improving the Accuracy of One-Shot Detectors for Small Objects in X-ray Images

Improving the Accuracy of One-Shot Detectors for Small Objec...

引用

International Russian Automation Conference (RusAutoCon)

作者： Polina Demochkina Andrey V. Savchenko National Research University Higher School of Economics Nizhny Novgorod Russia Laboratory of Algorithms and Technologies for Network Analysis National Research University Higher School of Economics Nizhny Novgorod Russia

The following topics are dealt with: control engineering computing; control system synthesis; optimisation; process control; electric drives; nonlinear control systems; motion control; machine control; production engi... 详细信息

ISBN: (数字)9781728161303

ISBN: (纸本)9781728161310

关键词： X-ray imaging Detectors Object detection Training Inspection Automation Weapons

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