scholarly journals FEATURE SELECTION USING ASSOCIATION RULES FOR CBIR AND COMPUTER AIDED MEDICAL DIAGNOSTIC

2015 ◽  
pp. 223-227
Author(s):  
C. BHUVANESWARI ◽  
P. ARUNA ◽  
D. LOGANATHAN
Keyword(s):  
Image Retrieval ◽  
Classification Accuracy ◽  
Visual Information ◽  
Naive Bayes ◽  
Diagnostic Techniques ◽  
Modern Medicine ◽  
Naïve Bayes ◽  
Content Based Image Retrieval ◽  
Sudden Increase ◽  
Computer Aided

Digital images are now the basis of visual information in medical applications. The advent of radiology which employs imaging for diagnosis generates great amount of images. Automatic retrieval of images based on features like color, shape and texture is termed Content Based Image Retrieval. The increasing dependence of modern medicine on diagnostic techniques such as radiology, computerized tomography has resulted in a sudden increase in the number and significance of medical images. Content Based Image Retrieval techniques are being extensively used to aid diagnosis by comparing with similar past cases and improvising Computer Aided Diagnosis. In this paper, it is proposed to extract features in the frequency domain using Walsh Hadamard transform and use FP-Growth association rule mining to extract features based on confidence. The extracted features are classified using Naïve Bayes and CART algorithms and the proposed method’s classification accuracy is evaluated. Experimental results show that classification accuracy for Naïve Bayes is 100 and 96.8 for CART on application of proposed method.

Retrieval of Multimedia Information Using Content-Based Image Retrieval (CBIR) Techniques

Biometrics ◽  
2017 ◽  
pp. 652-689
Author(s):  
Anupam Mukherjee
Keyword(s):  
Image Retrieval ◽  
Multimedia Information ◽  
Visual Information ◽  
Video Retrieval ◽  
Automatic Generation ◽  
Content Based Image Retrieval ◽  
Manual Labor ◽  
Multidimensional Indexing ◽  
Other Information ◽  
Human Operators

This chapter will focus on the concept of Content-based image retrieval. Searching of an image or video database based on text based description is a manual labor intensive process. Descriptions of the file are usually typed manually for each image by human operators because the automatic generation of keywords for the images is difficult without incorporation of visual information and feature extraction. This method is impractical in today's multimedia information era. “Content-based” means that the search will analyze the actual contents of the image rather than the metadata such as keywords, tags, and descriptions associated with the image. The term “content” in this context might refer to colors, shapes, textures, or any other information that can be derived from the image itself. Several important sections are highlighted in this chapter, like architectures, query techniques, multidimensional indexing, video retrieval and different application sections of CBIR.

Generic integration of content-based image retrieval in computer-aided diagnosis

2012 ◽  
Vol 108 (2) ◽  
pp. 589-599 ◽  
Author(s):  
Petra Welter ◽  
Benedikt Fischer ◽  
Rolf W. Günther ◽  
Thomas M. Deserno (né Lehmann)
Keyword(s):  
Image Retrieval ◽  
Computer Aided Diagnosis ◽  
Content Based Image Retrieval ◽  
Computer Aided ◽  
Aided Diagnosis

Computer-aided diagnostics of screening mammography using content-based image retrieval

2012 ◽  
Author(s):  
Thomas M. Deserno ◽  
Michael Soiron ◽  
Júlia E. E. de Oliveira ◽  
Arnaldo de A. Araújo
Keyword(s):  
Image Retrieval ◽  
Screening Mammography ◽  
Content Based Image Retrieval ◽  
Computer Aided

scholarly journals A REVIEW ON MULTIQUERY SYSTEM FOR CONTENT BASED IMAGE RETRIEVAL

2016 ◽  
Vol 15 (13) ◽  
pp. 7342-7346
Author(s):  
Meenu Meenu ◽  
Sonika Jindal
Keyword(s):  
Image Retrieval ◽  
Visual Information ◽  
Retrieval System ◽  
Image Similarity ◽  
Content Based Image Retrieval ◽  
Color Similarity ◽  
Image Retrieval System ◽  
Speed Up ◽  
Similarity Computation ◽  
Main Components

In recent years, very large collections of images and videos have grown rapidly. In parallel with this growth, content-based retrieval and querying the indexed collections are required to access visual information. Two of the main components of the visual information are texture and color. In this thesis, a content-based image retrieval system is presented that computes texture and color similarity among images. Content based image retrieval from large resources has become an area of wide interest now a days in many applications.  To speed up retrieval and similarity computation, the database images are analysed and the extracted regions are clustered according to their feature vectors. This process is performed offline before query processing, therefore to answer a query our system does not need to search the entire database images; instead just a number of candidate images are required to be searched for image similarity.

scholarly journals Analisis Sentimen Maskapai Penerbangan Menggunakan Metode Naive Bayes dan Seleksi Fitur Information Gain

2020 ◽  
Vol 7 (3) ◽  
pp. 599
Author(s):  
Arif Bijaksana Putra Negara ◽  
Hafiz Muhardi ◽  
Indira Melinda Putri
Keyword(s):  
Sentiment Analysis ◽  
Classification Accuracy ◽  
Naive Bayes ◽  
Information Gain ◽  
Naïve Bayes ◽  
Bayes Method ◽  
Review Section ◽  
User Reviews ◽  
Average Value

<p class="Abstrak">Zaman sekarang tren masyarakat untuk memesan tiket pesawat sudah melalui situs-situs <em>booking</em> <em>online</em>. Pegipegi.com merupakan salah satu <em>website</em> yang menyediakan pemesanan tiket dan menyediakan fitur ulasan bagi pengunjung untuk menyampaikan opini. Pengunjung lain yang membaca ulasan-ulasan tersebut dapat memperoleh gambaran secara lebih objektif mengenai maskapai penerbangan. Ulasan pengguna yang terdapat pada website pegipegi.com saat ini sudah sangat banyak sehingga hal ini menyulitkan dan memakan waktu untuk membaca secara keseluruhan. Oleh karena itu dirancang analisis sentimen guna membantu mengklasifikasi ulasan kedalam kategori positif atau negatif sehingga dapat memberikan rekomendasi maskapai penerbangan berdasarkan jumlah kategori ulasan. Metode yang diterapkan untuk klasifikasi sentimen adalah Naïve Bayes dengan seleksi fitur <em>Information Gain</em>. Adapun tujuan dari penelitian ini adalah mengetahui pengaruh dari pemilihan fitur <em>Information Gain</em> terhadap akurasi klasifikasi dan membuktikan bahwa metode Naïve Bayes dengan <em>Information Gain</em> dapat digunakan untuk klasifikasi analisis sentimen. Hasil pengujian yang telah dilakukan menunjukkan bahwa nilai rata-rata akurasi, <em>precision</em>, <em>recall</em> setelah penambahan <em>Information Gain</em> menunjukkan hasil yang lebih baik sebesar 0,865 jika dibandingkan sebelum penambahan information gain yakni sebesar 0,81.</p><p class="Abstrak"> </p><p class="Abstrak"><em><strong>Abstract</strong></em></p><p class="Abstrak"><em><em>Nowadays people tend to order airplane tickets through online booking sites. Pegipegi.com is a website that provides ticket reservations and a review section for visitors to express their opinions. Other visitors who read the reviews can get a more objective picture of airlines. The user reviews contained on the pegipegi.com website are currently very large so this makes it difficult and time consuming to read in its entirety. Therefore sentiment analysis is designed to help classify reviews into positive or negative categories so that they can provide airline recommendations based on the number of review categories. The method applied for sentiment classification is Naïve Bayes with the Information Gain feature selection. The purpose of this study was to determine the effect of selecting the Information Gain feature on classification accuracy and prove that the Naïve Bayes method with Information Gain can be used for the classification of sentiment analysis. The results of the tests that have been done show that the average value of accuracy, precision, recall after adding Information Gain shows better results of 0.865 compared to the addition of information gain which is equal to 0.81</em>.</em></p>

MapReduce Implementation of a Multinomial and Mixed Naive Bayes Classifier

2020 ◽  
Vol 16 (2) ◽  
pp. 1-23 ◽  
Author(s):  
Sikha Bagui ◽  
Keerthi Devulapalli ◽  
Sharon John
Keyword(s):  
Big Data ◽  
Gaussian Distribution ◽  
Classification Accuracy ◽  
Naive Bayes ◽  
Multinomial Distribution ◽  
Naïve Bayes ◽  
Probability Estimation ◽  
Bayes Classifier ◽  
Discrete Values ◽  
Block Sizes

This study presents an efficient way to deal with discrete as well as continuous values in Big Data in a parallel Naïve Bayes implementation on Hadoop's MapReduce environment. Two approaches were taken: (i) discretizing continuous values using a binning method; and (ii) using a multinomial distribution for probability estimation of discrete values and a Gaussian distribution for probability estimation of continuous values. The models were analyzed and compared for performance with respect to run time and classification accuracy for varying data sizes, data block sizes, and map memory sizes.

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