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H.264/SVC Mode Decision Based on Mode Correlation and Desired Mode List

International Journal of Automation and computing 2014年第5期11卷 510-516页

作者： L.Balaji k.k.thyagharajan Faculty of Information and Communication Anna University RMD Engineering College

Design of video encoders involves implementation of fast mode decision(FMD) algorithm to reduce computation complexity while maintaining the performance of the coding. Although H.264/scalable video coding(SVC) achieves high scalability and coding efficiency, it also has high complexity in implementing its exhaustive computation. In this paper, a novel algorithm is proposed to reduce the redundant candidate modes by making use of the correlation among layers. A desired mode list is created based on the probability to be the best mode for each block in base layer and a candidate mode selection in the enhancement layer by the correlations of modes among reference frame and current frame. Our algorithm is implemented in joint scalable video model(JSVM)9.19.15 reference software and the performance is evaluated based on the average encoding time, peak signal to noise ration(PSNR)and bit rate. The experimental results show 41.89% improvement in encoding time with minimal loss of 0.02 dB in PSNR and 0.05%increase in bit rate.

关键词： H.264 scalable video coding mode decision mode correlation rate distortion cost

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Image Manipulation Detection Through Laterally Linked Pixels and kernel Algorithms

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Computer Systems Science & Engineering 2022年第4期41卷 357-371页

作者： k.k.thyagharajan G.Nirmala RMD Engineering College KavaraipettaiChennaiIndia

In this paper,copy-move forgery in image is detected for single image with multiple manipulations such as blurring,noise addition,gray scale conver-sion,brightness modiﬁcations,rotation,Hu adjustment,color adjustment,contrast changes and JPEG ***,traditional algorithms detect only copy-move attacks in image and never for different manipulation in single *** proposed LLP(Laterally linked pixel)algorithm has two dimensional arrays and single layer is obtained through unit linking pulsed neural network for detec-tion of copied region and kernel tricks is applied for detection of multiple manip-ulations in single forged *** algorithm consists of two channels such as feeding component(F-Channel)and linking component(L channel)for linking *** algorithm linking pixels detects image with multiple manipulation and copy-move forgery due to one-to-one correspondence between pixel and neu-ron,where each pixel’s intensity is taken as input for F channel of neuron and connected for forgery identiﬁ***,neuron is connected with neighboringﬁeld of neuron by L channel for detecting forged images with multi-ple manipulations in the image along with copy-move,through kernel trick clas-siﬁer(kTC).From experimental results,proposed LLP algorithm performs better than traditional algorithms for multiple manipulated copy and paste *** accuracy obtained through LLP algorithm is about 90%and further forgery detec-tion is improved based on optimized kernel selections in classiﬁcation algorithm.

关键词： Machine learning copy move forgery support vectors kernel feature extraction

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Diagnosis of Neem Leaf Diseases Using Fuzzy-HOBINM and ANFIS Algorithms

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Computers, Materials & Continua 2021年第11期69卷 2061-2076页

作者： k.k.thyagharajan I.kiruba Raji r.m.d engineering college kavaraipettaigummidipoonditriuvallurindia

This paper proposes an approach to detecting diseases in neem leaf that uses a Fuzzy-Higher Order Biologically Inspired Neuron Model(F-HOBINM)and adaptive neuro classifier(ANFIS).India exports USD 0.28-million worth of neem leaf to the Uk,USA,UAE,and Europe in the form of dried leaves and powder,both of which help reduce diabetesrelated issues,cardiovascular problems,and eye *** neem leaf disease is difficult through visual interpretation,owing to similarity in their color and texture *** most common diseases include bacterial blight,Colletotrichum and Alternaria leaf spot,blight,damping-off,powdery mildew,Pseudocercospora leaf spot,leaf web blight,and seedling ***,traditional color and texture algorithms fail to identify leaf diseases due to irregular lumps and surfaces,and rough ridges,as the classification time involved takes as long as a *** proposed F-HOBINM algorithm recognizes the leaf intensity through the leaky capacitor,and uses subjective intensity and physical stimulus to interpret the ***,the processed leaf images from the HOBINM algorithm are applied to the ANFIS classifier to identify neem leaf *** experimental results show 92.18%accuracy from a database of 1,462 neem leaves.

关键词： Higher-order neural network fuzzy c-means clustering Mamdani fuzzy inference system adaptive neuro-fuzzy classifier

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SF-CNN: Deep Text Classification and Retrieval for Text Documents

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Intelligent Automation & Soft Computing 2023年第2期35卷 1799-1813页

作者： R.Sarasu k.k.thyagharajan N.R.Shanker Computer Science and Engineering Dhanalaksmi College of EngineeringAnna UniversityChennaiIndia R.M.D Engineering College Anna UniversityChennaiIndia Computer Science and Engineering Aalim Muhammed Salegh College of EngineeringAnna UniversityChennaiIndia

Researchers and scientists need rapid access to text documents such as research papers,source code and *** research documents are available on the Internet and need more time to retrieve exact documents based on *** efficient classification algorithm for retrieving documents based on keyword words is *** traditional algorithm performs less because it never considers words’polysemy and the relationship between bag-of-words in *** solve the above problem,Semantic Featured Convolution Neural Networks(SF-CNN)is proposed to obtain the key relationships among the searching keywords and build a structure for matching the words for retrieving correct text *** proposed SF-CNN is based on deep semantic-based bag-of-word representation for document *** deep learning methods such as Convolutional Neural Network and Recurrent Neural Network never use semantic representation for *** experiment is performed with different document datasets for evaluating the performance of the proposed SF-CNN ***-CNN classifies the documents with an accuracy of 94%than the traditional algorithms.

关键词： Semantic classiﬁcation convolution neural networks semantic enhancement

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Semantic Rule Based Image Visual Feature Ontology Creation

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International Journal of Automation and computing 2014年第5期11卷 489-499页

作者： R. I. Minu k. k. thyagharajan Department of Computer Science & Engineering Jerusalem College of Engineering RMD Engineering College

Multimedia is one of the important communication channels for mankind. Due to the advancement in technology and enormous growth of mankind, a vast array of multimedia data is available today. This has resulted in the obvious need for some techniques for retrieving these data. This paper will give an overview of ontology-based image retrieval system for asteroideae flower family domain. In order to reduce the semantic gap between the low-level visual features of an image and the high-level domain knowledge, we have incorporated a concept of multi-modal image ontology. So, the created asteroideae flower domain specific ontology would have the knowledge about the domain and the visual features. The visual features used to define the ontology are prevalent color,basic intrinsic pattern and contour gradient. In prevalent color extraction, the most dominant color from the images was identified and indexed. In order to determine the texture pattern for a particular flower, basic intrinsic patterns were used. The contour gradients provide the information on the image edges with respect to the image base. These feature values are embedded in the ontology at appropriate slots with respect to the domain knowledge. This paper also defines some of the query axioms which are used to retrieve appropriate information from the created ontology. This ontology can be used for image retrieval system in semantic web.

关键词： Information and knowledge computer vision intelligent computing feature extraction ontology

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