Content-Based Image Retrieval Using Hybrid Feature Extraction Techniques

Akshaya, B.; Sri, Suvetha; Sathish, A. Niranjana; Shobika, K.; Karthika, R.; Parameswaran, Latha

doi:10.1007/978-3-030-00665-5_58

Cited by 3 publications

(2 citation statements)

References 11 publications

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“…Extracting metadata from pdf using open source java framework stores extracted data in Oracle and XML (Extensible Markup language) files [11]. A content-based image retrieval system using primary features of images will check similarity for classification [12]. Detecting vehicles in traffic using advanced surveillance techniques using deep learning techniques reduces noise and provides better performance [13].…”

Section: Introductionmentioning

confidence: 99%

Abstract and Image Analysis of High-Temperature Materials from Scientific Journals Using Deep Learning and Rule-Based Machine Learning Approaches

Jayaram

Prakash

Vishakantaiah

2021

Lecture Notes in Electrical Engineering

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In this digital world, many research papers have been getting added to the scientific journals in PDF format. It has become essential to the process and needs digitalization due to the exponential growth of newly published research papers. Survey of Abstract, Figures, and Captions convey the necessary information and extracted from scientific research journals. The section algorithm is used for extracting "Abstract" and "Result and Discussion" sections from the published research papers about high-temperature materials (HTM) by converting PDF to word documents. HTM got complete applications in aerospace engineering and electronic. After exposure to high-temperature test facilities, various characterization techniques are used to produce numerous high-resolution images. Abstract summarization, SEM figures, and caption extraction from HTM documents are performed using Deep learning, PDFFigure, and Machine learning (ML) techniques. MatLab is used for feature extraction and to classify SEM images by rule-based ML techniques. This summarization helps new researchers understand existing research datasets and proceed with new approaches to solve more complex problems. This research paper presents automatic extraction and summary of abstract, high-resolution figures, and captions from scientific journals on HTM; all these documents will be analyzed to categorize with a different tailor-made approach using deep learning and machine learning techniques.

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Section: Introductionmentioning

confidence: 99%

Abstract and Image Analysis of High-Temperature Materials from Scientific Journals Using Deep Learning and Rule-Based Machine Learning Approaches

Jayaram

Prakash

Vishakantaiah

2021

Lecture Notes in Electrical Engineering

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“…A function of appreciation using the acknowledgment section within academia of the instrumental and normative significance has been presented [9]. A content-based image retrieval system that extracts, image features from journal papers using a supervised learning algorithm has been explained in [10]. An overview of the principles and methods of automatic term recognition of significant elements have been presented [11].…”

Section: Introductionmentioning

confidence: 99%

Automatic Extraction of Rarely Explored Materials and Methods Sections from Research Journals using Machine Learning Techniques

Jayaram¹,

Prakash²,

Jayaram³

2020

IJACSA

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The scientific community is expanding by leaps and bounds every day owing to pioneering and path breaking scientific literature published in journals around the globe. Viewing as well as retrieving this data is a challenging task in today's fast paced world. The essence and importance of scientific research papers for the expert lies in their experimental and theoretical results along with the sanctioned research projects from the organizations. Since scant work has been done in this direction, the alternative option is to explore text mining by machine learning techniques. Myriad journals are available on material research which throws light on a gamut of materials, synthesis methods, and characterization methods used to study properties of the materials. Application of materials has many diversified areas, hence selected papers from "Journal of Material Science" where "Materials and Methods" sections contains names of the method, characterization techniques (instrumental methods), algorithms, images, etc. used in research work. The "Acknowledgment" section conveys information about authors' proximity, collaborations with organizations that are again not explored for the citation network. In the present articulated work, our attempt is to derive a means to automatically extract methods or terminologies used in characterization techniques, author, organization data from "Materials and Methods" and "Acknowledgment" sections, using machine learning techniques. Another goal of this research is to provide a data set for characterization terms, classification and an extended version of the existing citation network for material research. The complete dataset will help new researchers to select research work, find new domains and techniques to solve advanced scientific research problems.

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A Multilevel Transfer Learning Technique and LSTM Framework for Generating Medical Captions for Limited CT and DBT Images

Aswiga

Shanthi

2022

J Digit Imaging

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Content-Based Image Retrieval Using Hybrid Feature Extraction Techniques

Cited by 3 publications

References 11 publications

Abstract and Image Analysis of High-Temperature Materials from Scientific Journals Using Deep Learning and Rule-Based Machine Learning Approaches

Abstract and Image Analysis of High-Temperature Materials from Scientific Journals Using Deep Learning and Rule-Based Machine Learning Approaches

Automatic Extraction of Rarely Explored Materials and Methods Sections from Research Journals using Machine Learning Techniques

A Multilevel Transfer Learning Technique and LSTM Framework for Generating Medical Captions for Limited CT and DBT Images

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