2021 Sixth International Conference on Image Information Processing (ICIIP)最新文献_第10页

Experimentation with NMT models on low resource Indic languages NMT模型在低资源印度语上的实验

2021 Sixth International Conference on Image Information Processing (ICIIP) Pub Date : 2021-11-26 DOI: 10.1109/ICIIP53038.2021.9702577

Nikunj Bansal, Goutam Datta, Ashutosh Kumar Singh

{"title":"Experimentation with NMT models on low resource Indic languages","authors":"Nikunj Bansal, Goutam Datta, Ashutosh Kumar Singh","doi":"10.1109/ICIIP53038.2021.9702577","DOIUrl":"https://doi.org/10.1109/ICIIP53038.2021.9702577","url":null,"abstract":"Today’s Artificial Intelligence (AI) is data centric. Unlike earlier rule based systems where we used to write many rules to solve any specific problem, these days, we need to train our machine learning models with the help of huge corpus (data set). In this paper, we have discussed one of the important applications of AI and computational linguistic i.e. Machine Translation (MT) which translates one natural language to another automatically.MT industry has passed through different phases since its earlier popular approach such as statistical machine translation (SMT) systems and its other version such as phrase based SMT. Performance wise NMT always outperformed SMT on various aspects. However, this holds true only for the languages having large parallel corpora. For low-resource languages, it still remains suboptimal. In this paper, we have applied NMT to low resources Indian languages, i.e. English-Hindi. We used a basic LSTM based Seq2Seq model and an attention-based Seq2Seq model with fixed vocabulary size. We merged the corpus collected from various sources and preprocessed them for further use. We used the BLEU metric score for evaluation. We also evaluated the Google Translator to compare our experimental results with it.","PeriodicalId":431272,"journal":{"name":"2021 Sixth International Conference on Image Information Processing (ICIIP)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127702041","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Building Adaptive Software Reusable Components Using Domain Engineering 使用领域工程构建自适应软件可重用组件

2021 Sixth International Conference on Image Information Processing (ICIIP) Pub Date : 2021-11-26 DOI: 10.1109/ICIIP53038.2021.9702594

Sampath Korra, V. Biksham, Nagunuri Rajender, Tippireddy Chalama Reddy

引用次数: 0

Identification of Tampering Image Using SIFT Descriptor 基于SIFT描述符的篡改图像识别

2021 Sixth International Conference on Image Information Processing (ICIIP) Pub Date : 2021-11-26 DOI: 10.1109/ICIIP53038.2021.9702601

Debjani Chakraborty, Sandeep Choudhury, Sanjib Kumar Dutta, Biswajit Haldar

{"title":"Identification of Tampering Image Using SIFT Descriptor","authors":"Debjani Chakraborty, Sandeep Choudhury, Sanjib Kumar Dutta, Biswajit Haldar","doi":"10.1109/ICIIP53038.2021.9702601","DOIUrl":"https://doi.org/10.1109/ICIIP53038.2021.9702601","url":null,"abstract":"In the recent era, image tampering has become one of the threatening security problems in digital platforms. There are many software’s available for tampering with an image that depicts as an original image. Different tampering techniques are used to hide important portions from an image or document, one very common practice is a copy-move forgery that is quite impossible to distinguish with an open eye. Authentications of such images are an ardent research area in image processing and computer vision but still a challenging problem. This paper presents a method to identify image tampering that is based on SIFT (Scale Invariant Feature Transform) algorithm. SIFT descriptor is used to extract keypoint features from the input image and a hierarchical clustering algorithm is used to improve the accuracy of identifying the tampered location. The execution time of our proposed method is proportional to image resolution. If one portion of the image is copied and pasted on multiple locations on the same image, our proposed method can identify such occurrences. Finally, Homography is used to show the tampering points and their matching.","PeriodicalId":431272,"journal":{"name":"2021 Sixth International Conference on Image Information Processing (ICIIP)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130770233","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Robust Color Image Watermarking Scheme with High Payload Capacity using FRT - SVD 基于FRT - SVD的高有效载荷鲁棒彩色图像水印方案

2021 Sixth International Conference on Image Information Processing (ICIIP) Pub Date : 2021-11-26 DOI: 10.1109/ICIIP53038.2021.9702542

Rohit M. Thanki, Purva Joshi

{"title":"Robust Color Image Watermarking Scheme with High Payload Capacity using FRT - SVD","authors":"Rohit M. Thanki, Purva Joshi","doi":"10.1109/ICIIP53038.2021.9702542","DOIUrl":"https://doi.org/10.1109/ICIIP53038.2021.9702542","url":null,"abstract":"When any color image transfers through an open-source communication channel, then the security of the color image is compromised against various manipulations. Therefore, the attacker has easily stolen any important color image when transferred from one place to another. For this problem, one of the solutions is digital watermarking, which provides security when data communicate through a channel. But most existing watermarking schemes can hide a small amount of owner identity into a color image to generate a secure watermarked color image. In this paper, a ridgelet transform and singular value decomposition-based watermarking scheme is proposed to tackle this problem, which provides higher payload capacity. Here, the color watermark logo is inserted into singular value of ridgelet coefficients of the color cover image to get the color watermarked image. After that, scrambling-based encryption is applied to it to get an encrypted color watermarked image that provides security before transmission. The experimental results of the proposed scheme show that this new scheme offers better payload capacity and performs better than existing color watermarking schemes.","PeriodicalId":431272,"journal":{"name":"2021 Sixth International Conference on Image Information Processing (ICIIP)","volume":"113 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134402353","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Anti-spoofing Performance Enhancement by Facial Micro-expression Detection using Kinect Sensor 基于Kinect传感器的面部微表情检测增强抗欺骗性能

2021 Sixth International Conference on Image Information Processing (ICIIP) Pub Date : 2021-11-26 DOI: 10.1109/ICIIP53038.2021.9702679

A. Pal, Debmani Saha

引用次数: 0

An Efficient Brain Tumor Detection Using Modified Tree Growth Algorithm and Random Forest Method 基于改进树生长算法和随机森林方法的脑肿瘤检测

2021 Sixth International Conference on Image Information Processing (ICIIP) Pub Date : 2021-11-26 DOI: 10.1109/ICIIP53038.2021.9702692

Ratima Raj Singh, Surbhi Vijh, Diwakar Chaubey

{"title":"An Efficient Brain Tumor Detection Using Modified Tree Growth Algorithm and Random Forest Method","authors":"Ratima Raj Singh, Surbhi Vijh, Diwakar Chaubey","doi":"10.1109/ICIIP53038.2021.9702692","DOIUrl":"https://doi.org/10.1109/ICIIP53038.2021.9702692","url":null,"abstract":"Medical image processing techniques are believed to diagnose the tumor to improve the patient's survival rate. This paper proposed an improved nature-based Tree Growth algorithm (TGA) for selecting the finest features from the feature set derived by the Local Binary Pattern (LBP) and Gray level co-occurrence matrix (GLCM), the images from Brain Magnetic resonance images (MRI) classified as tumor or non-tumor by Random Forest (RF) classifier. The significance of this paper is the creation of an intelligent brain tumor diagnostic system using a Chaotic modified binary tree growth algorithm (CMBTGA-RF). The chaotic map, crossover, and mutation operators are implemented in an updated binary tree growth algorithm for improving exploitation and exploration behaviours. The performance of the proposed methodology is assessed with accuracy, sensitivity, specificity, precision, negative predictive value (NPV), and F-score of 96.42%, 100%, 94.11%, 91.66%, 100%, and 96.96% respectively. The algorithmic findings demonstrate that the CMBTGA-RF with Logistic map works better than the nature-based traditional and recent meta-heuristic algorithms.","PeriodicalId":431272,"journal":{"name":"2021 Sixth International Conference on Image Information Processing (ICIIP)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125205934","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Technical Programme Schedule for ICIIP-2021 ICIIP-2021技术方案时间表

2021 Sixth International Conference on Image Information Processing (ICIIP) Pub Date : 2021-11-26 DOI: 10.1109/iciip53038.2021.9702589

引用次数: 0

Big Data Analytics in Cloud Computing 云计算中的大数据分析

2021 Sixth International Conference on Image Information Processing (ICIIP) Pub Date : 2021-11-26 DOI: 10.1109/ICIIP53038.2021.9702705

N. Suhasini, Srilatha Puli

{"title":"Big Data Analytics in Cloud Computing","authors":"N. Suhasini, Srilatha Puli","doi":"10.1109/ICIIP53038.2021.9702705","DOIUrl":"https://doi.org/10.1109/ICIIP53038.2021.9702705","url":null,"abstract":"In this paper we will study the two unlike but related technologies – Big Data and cloud computing – and also examines the benefits and outcomes of using cloud computing for Big Data analytics. As information is being produced at a phenomenal scale and it is originating from every direction, such a monstrous measure of information makes enormous or complex informational indexes. These informational collections are known as Big Data. The blend of cloud and big data can be attributed to fresh IT (Information Technology) wave that is causing remarkable progress across IT departments of various industries. In this new environment of big data and cloud there are challenges associated with data storage as data are accumulating rapidly.With rapidly expansion of Big data requires government agencies to extract relevant data and make sense of it in order to make evidence-based policy decisions, with a focus on translating data into information and subsequently information into knowledge. The way to do this is to employ so-called big data analytics, which involves analyzing numerous data sets in order to reveal information such as specific patterns, correlations, and trends, among other things.Big data analytics places rigorous needs on networks, storage, and servers. This is why few businesses use the cloud for this. Big data and cloud are combining to create new business prospects that support big data research while also overcoming numerous architectural challenges.For these mutually exclusive principles to coexist a solution architecture is required to truly exploit. Future breakthroughs and research difficulties in cloud computing that support Big Data analytics are presented in this review.","PeriodicalId":431272,"journal":{"name":"2021 Sixth International Conference on Image Information Processing (ICIIP)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131307135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

A color image watermarking in the frequency domain using a teaching-learning optimization algorithm 基于教-学优化算法的彩色图像频域水印

2021 Sixth International Conference on Image Information Processing (ICIIP) Pub Date : 2021-11-26 DOI: 10.1109/ICIIP53038.2021.9702645

Nikhlesh Kumar Badoga, Raman Kumar Goyal, R. Mehta

{"title":"A color image watermarking in the frequency domain using a teaching-learning optimization algorithm","authors":"Nikhlesh Kumar Badoga, Raman Kumar Goyal, R. Mehta","doi":"10.1109/ICIIP53038.2021.9702645","DOIUrl":"https://doi.org/10.1109/ICIIP53038.2021.9702645","url":null,"abstract":"This paper presents a color image watermark technique that employs teaching learning-based optimization algorithm (TLBO) and lagrangian twin support vector regression (LTSVR) in the frequency domain. By analyzing the statistical property of the selected wavelet band (LL sub-band) after single-level decomposition, LTSVR is used for extraction of watermark and embedding processes. TLBO is used to find the optimal value of watermark strength for different selected blocks of the image in the wavelet domain. Various kinds of images are considered to test the imperceptibility and robustness of the watermark in experimental results. The metric Peak Signal to Noise Ratio (PSNR) has been used for watermark images to evaluate the: (i) imperceptibility, (ii) quality. Bit error rate (BER) and normalized correlation (NC) value is computed to determine the effectiveness and standards of the extracted watermark. JPEG compression attack with different quality factors (QF) ranging from 10 to 90 is evaluated using robustness to determine the proficiency of the proposed work. Experimental results show that the proposed method is robust to JPEG compression as compared to state of art method.","PeriodicalId":431272,"journal":{"name":"2021 Sixth International Conference on Image Information Processing (ICIIP)","volume":"120 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115823167","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Cloud Computing: A relevant Solution for Drug Designing using different Software’s 云计算:使用不同软件进行药物设计的相关解决方案

2021 Sixth International Conference on Image Information Processing (ICIIP) Pub Date : 2021-11-26 DOI: 10.1109/ICIIP53038.2021.9702618

Tejinder Kaur, Divya Dhawal Bhandari, Rajiv Sharma

引用次数: 0