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New texture-spatial features for keyword spotting in video images

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New texture-spatial features for keyword spotting in video i...

Asian Conference on pattern recognition (ACPR)

作者： Palaiahnakote Shivakumara Guozhu Liang Sangheeta Roy Umapada Pal Tong Lu Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia National Key Lab for Novel Software Technology Nanjing University Nanjing China Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

ISBN: (纸本)9781479961016

Keyword spotting in video document images is challenging due to low resolution and complex background of video images. We propose the combination of Texture-Spatial-Features (TSF) for keyword spotting in video images without recognizing them. First, a segmentation method extracts words from text lines in each video image. Then we propose the set of texture features for identifying text candidates in the word image with the help of k-means clustering. The proposed method finds proximity between text candidates to study the spatial arrangement of pixels that result in feature vectors for spotting words in the input frame. The proposed method is evaluated on word images of different fonts, contrasts, backgrounds and font sizes, which are chosen from standard databases such as ICDAR 2013 video and our video data. Experimental results show that the proposed method outperforms the existing method in terms of recall, precision and f-measure.

关键词： Image segmentation Semantics Video signal processing Indexing pattern recognition Spatial resolution

DCT-phase statistics for forged IMEI numbers and air ticket detection

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Expert Systems with Applications 2021年 164卷 114014-114014页

作者： Nandanwar, Lokesh Shivakumara, Palaiahnakote Kanchan, Swati Basavaraja, V. Guru, D.S. Pal, Umapada Lu, Tong Blumenstein, Michael Faculty of Computer Science and Information Technology University of Malaya Malaysia Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Department of Studies in Computer Science University of Mysore Karnataka India National Key Lab for Novel Software Technology Nanjing University China Sydney Australia

New tools have been developing with the intention of having more flexibility and greater user-friendliness for editing the images and documents in digital technologies, but, unfortunately, they are also being used for manipulating and tampering information. Examples of such crimes include creating forged International Mobile Equipment Identity (IMEI) numbers which are embedded on mobile packages and inside smart mobile cases for illicit activities. Another example of such crimes is altering the name or date on air tickets for breaching security at the airport. This paper presents a new expert system for detecting forged IMEI numbers as well as altered air ticket images. The proposed method derives the phase spectrum using the Discrete Cosine Transform (DCT) to highlight the suspicious regions;it is unlike the phase spectrum from a Fourier transform, which is ineffective due to power spectrum noise. From the phase spectrum, our method extracts phase statistics to study the effect of distortions introduced by forgery operations. This results in feature vectors, which are fed to a Support Vector Machine (SVM) classifier for detection of forged IMEI numbers and air ticket images. Experimental results on our dataset of forged IMEI numbers (which is created by us for this work), on altered air tickets, on benchmark datasets of video caption text (which is tampered text), and on altered receipts of the ICPR 2018 FDC dataset, show that the proposed method is robust across different datasets. Furthermore, comparative studies of the proposed method with the existing methods on the same datasets show that the proposed method outperforms the existing methods. The dataset created will be available freely on request to the authors. © 2020 Elsevier Ltd

关键词： Discrete cosine transforms

SigNet: Convolutional siamese network for writer independent offline signature verification

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arXiv 2017年

作者： Dey, Sounak Dutta, Anjan Ignacio Toledo, J. Ghosh, Suman K. Lladós, Josep Pal, Umapada Computer Vision Center Computer Science Dept. Universitat Autònoma de Barcelona Edifici O Campus Uab Bellaterra08193 Spain Computer Vision and Pattern Recognition Unit Indian Statistical Institute 203 B. T. Road Kolkata700108 India

Offline signature verification is one of the most challenging tasks in biometrics and document foren-sics. Unlike other verification problems, it needs to model minute but critical details between genuine and forged signatures, because a skilled falsification might only differ from a real signature by some specific kinds of deformation. This verification task is even harder in writer independent scenarios which is undeniably fiscal for realistic cases. In this paper, we model an offline writer independent signature verification task with a convolutional Siamese network. Siamese networks are twin networks with shared weights, which can be trained to learn a feature space where similar observations are placed in proximity. This is achieved by exposing the network to a pair of similar and dissimilar observations and minimizing the Euclidean distance between similar pairs while simultaneously maximizing it between dissimilar pairs. Experiments conducted on cross-domain datasets emphasize the capability of our network to handle forgery in different languages (scripts) and handwriting styles. Moreover, our designed Siamese network, named SigNet, provided better results than the state-of-the-art results on most of the benchmark signature datasets. Copyright © 2017, The Authors. All rights reserved.

关键词： Convolution

Non-Uniform Illumination Attack for Fooling Convolutional Neural Networks

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arXiv 2024年

作者： Jain, Akshay Dubey, Shiv Ram Singh, Satish Kumar Santosh, K.C. Chaudhuri, Bidyut Baran The Computer Vision and Biometrics Lab Department of Information Technology Indian Institute of Information Technology Allahabad Uttar Pradesh Prayagraj211015 India The AI Research Lab Department of Computer Science University of South Dakota VermillionSD57069 United States The Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata700108 India

Convolutional Neural Networks (CNNs) have made remarkable strides;however, they remain susceptible to vulnerabilities, particularly in the face of minor image perturbations that humans can easily recognize. This weakness, often termed as ‘attacks,’ underscores the limited robustness of CNNs and the need for research into fortifying their resistance against such manipulations. This study introduces a novel Non-Uniform Illumination (NUI) attack technique, where images are subtly altered using varying NUI masks. Extensive experiments are conducted on widely-accepted datasets including CIFAR10, TinyImageNet, and CalTech256, focusing on image classification with 12 different NUI attack models. The resilience of VGG, ResNet, MobilenetV3-small and InceptionV3 models against NUI attacks are evaluated. Our results show a substantial decline in the CNN models’ classification accuracy when subjected to NUI attacks, indicating their vulnerability under non-uniform illumination. To mitigate this, a defense strategy is proposed, including NUI-attacked images, generated through the new NUI transformation, into the training set. The results demonstrate a significant enhancement in CNN model performance when confronted with perturbed images affected by NUI attacks. This strategy seeks to bolster CNN models’ resilience against NUI attacks. 1 Copyright © 2024, The Authors. All rights reserved.

关键词： Convolutional neural networks

FWLBP: A scale invariant descriptor for texture classification

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arXiv 2018年

作者： Roy, Swalpa Kumar Bhattacharya, Nilavra Chanda, Bhabatosh Chaudhuri, Bidyut B. Ghosh, Dipak Kumar Optical Character Recognition Laboratory Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata700108 India School of Information University of Texas AustinTX78712 United States Image Processing Laboratory Electronics and Communication Sciences Unit Indian Statistical Institute Kolkata700108 India Department of Electronics and Communication Engineering National Institute of Technology Rourkela Rourkela769008 India

In this paper we propose a novel texture descriptor called Fractal Weighted Local Binary pattern (FWLBP). The fractal dimension (FD) measure is relatively invariant to scale-changes, and presents a good correlation with human viewpoint of surface roughness. We have utilized this property to construct a scale-invariant descriptor. Here, the input image is sampled using an augmented form of the local binary pattern (LBP) over three different radii, and then used an indexing operation to assign FD weights to the collected samples. The final histogram of the descriptor has its features calculated using LBP, and its weights computed from the FD image. The proposed descriptor is scale invariant, and is also robust in rotation or reflection, and partially tolerant to noise and illumination changes. In addition, the local fractal dimension is relatively insensitive to the bi-Lipschitz transformations, whereas its extension is adequate to precisely discriminate the fundamental of texture primitives. Experiment results carried out on standard texture databases show that the proposed descriptor achieved better classification rates compared to the state-of-the-art descriptors. Copyright © 2018, The Authors. All rights reserved.

关键词： Fractal dimension

ICFHR 2012 Competition on recognition of On-Line Mathematical Expressions (CROHME 2012)

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ICFHR 2012 Competition on Recognition of On-Line Mathematica...

International Workshop on Frontiers in Handwriting recognition

作者： Harold Mouchère Christian Viard-Gaudin D.H. Kim J.H. Kim U. Garain IRCCyN/IVC-UMR CNRS 6597 Ecole Polytechnique de l'Iniversité de Nantes France Division of Computer Science Korea Advanced Institute of Science and Technology South Korea Computer Vision and Pattern Recognition (CVPR) Unit Indian Statistical Institute India

This paper presents an overview of the second Competition on recognition of Online Handwritten Mathematical Expressions, CROHME 2012. The objective of the contest is to identify current advances in mathematical expression recognition using common evaluation performance measures and datasets. This paper describes the contest details including the evaluation measures used as well as the performance of the 7 submitted systems along with a short description of each system. Progress as compared to the 1st version of CROHME is also documented.

关键词： Handwriting recognition

diffGrad: An optimization method for convolutional neural networks

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arXiv 2019年

作者： Dubey, Shiv Ram Chakraborty, Soumendu Roy, Swalpa Kumar Mukherjee, Snehasis Singh, Satish Kumar Chaudhuri, Bidyut Baran The Computer Vision Group Indian Institute of Information Technology Sri City Andhra Pradesh Chittoor517646 India The Indian Institute of Information Technology Uttar Pradesh Lucknow India The Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata700108 India Techno India University Sector V Salt Lake City Kolkata700091 India The Computer Vision and Biometrics Laboratory Indian Institute of Information Technology Allahabad211015 India

Stochastic Gradient Decent (SGD) is one of the core techniques behind the success of deep neural networks. The gradient provides information on the direction in which a function has the steepest rate of change. The main problem with basic SGD is to change by equal sized steps for all parameters, irrespective of gradient behavior. Hence, an efficient way of deep network optimization is to make adaptive step sizes for each parameter. Recently, several attempts have been made to improve gradient descent methods such as AdaGrad, AdaDelta, RMSProp and Adam. These methods rely on the square roots of exponential moving averages of squared past gradients. Thus, these methods do not take advantage of local change in gradients. In this paper, a novel optimizer is proposed based on the difference between the present and the immediate past gradient (i.e., diffGrad). In the proposed diffGrad optimization technique, the step size is adjusted for each parameter in such a way that it should have a larger step size for faster gradient changing parameters and a lower step size for lower gradient changing parameters. The convergence analysis is done using the regret bound approach of online learning framework. Rigorous analysis is made in this paper over three synthetic complex non-convex functions. The image categorization experiments are also conducted over the CIFAR10 and CIFAR100 datasets to observe the performance of diffGrad with respect to the state-of-the-art optimizers such as SGDM, AdaGrad, AdaDelta, RMSProp, AMSGrad, and Adam. The residual unit (ResNet) based Convolutional Neural Networks (CNN) architecture is used in the experiments. The experiments show that diffGrad outperforms other optimizers. Also, we show that diffGrad performs uniformly well for training CNN using different activation functions. The source code is made publicly available at https://***/shivram1987/diffGrad. Copyright © 2019, The Authors. All rights reserved.

关键词： Optimization

Gradient-Angular-Features for Word-wise Video Script Identification

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Gradient-Angular-Features for Word-wise Video Script Identif...

International Conference on pattern recognition

作者： Palaiahnakote Shivakumara Nabin Sharma Umapada Pal Michael Blumenstein Chew Lim Tan Faculty of Computer Science University of Malaya Kuala Lumpur Malaysia 'Griffith University Gold Coast Queensland Australia Cornputcr Vision and Pattern Recognition Unit Indian Statistical Institute India School of Computing National University of Singapore Singapore

ISBN: (纸本)9781479952106

Script identification at the word level is challenging because of complex backgrounds and low resolution of video. The presence of graphics and scene text in video makes the problem more challenging. In this paper, we employ gradient angle segmentation on words from video text lines. This paper presents new Gradient-Angular-Features (GAF) for video script identification, namely, Arabic, Chinese, English, Japanese, Korean and Tamil. This work enables us to select an appropriate OCR when the frame has words of multi-scripts. We employ gradient directional features for segmenting words from video text lines. For each segmented word, we study the gradient information in effective ways to identify text candidates. The skeleton of the text candidates is analyzed to identify Potential Text Candidates (PTC) by filtering out unwanted text candidates. We propose novel GAF for the PTC to study the structure of the components in the form of cursiveness and softness. The histogram operation on the GAF is performed in different ways to obtain discriminative features. The method is evaluated on 760 words of six scripts having low contrast, complex background, different font sizes, etc. in terms of the classification rate and is compared with an existing method to show the effectiveness of the method. We achieve 88.2% average classification rate.

关键词： Feature extraction Histograms Equations Mathematical model Shape Support vector machine classification pattern recognition

Inception-based Deep Learning Architecture for Tuberculosis Screening using Chest X-rays

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Inception-based Deep Learning Architecture for Tuberculosis ...

International Conference on pattern recognition

作者： Dipayan Das K.C. Santosh Umapada Pal National Institute of Technology Durgapur India University of South Dakota Vermillion SD USA Computer Vision & Pattern Rec. Unit Indian Statistical Institute Kolkata India

ISBN: (纸本)9781728188089;9781728188096

The motivation for this work is the primary need of screening Tuberculosis (TB) positive patients in the severely resource constrained regions of the world. Chest X-ray (CXR) is considered to be a promising indicator for the onset of TB, but the lack of skilled radiologists in such resource constrained regions degrades the situation. Therefore, several computer aided diagnosis (CAD) systems have been proposed to solve the decision making problem, which includes hand-engineered feature extraction methods to deep learning or Convolutional Neural Network (CNN) based methods. Feature extraction, being a time and resource intensive process, often delays the process of mass screening. Hence, an end to end CNN architecture is proposed in this work to solve the problem. Two benchmark CXR datasets have been used in this work, collected from Shenzhen (China) and Montgomery County (USA), on which the proposed methodology achieved a maximum abnormality detection accuracy (ACC) of 91.7% (0.96 AUC) and 87.47% (0.92 AUC) respectively. Considering these datasets, to the best of our knowledge, the obtained results are superior to the state of the art deep learning based works.

关键词： Deep learning Solid modeling Analytical models Design automation Computational modeling Face recognition Feature extraction