scholarly journals Semantic Learning and Web Image Mining with Image Recognition and Classification

2012 ◽  
pp. 159-163
Author(s):  
Lambodar Jena ◽  
Ramakrushna Swain ◽  
N.K. Kamila
Keyword(s):  
Image Classification ◽  
Image Recognition ◽  
Web Mining ◽  
Image Data ◽  
Support Vector ◽  
Image Mining ◽  
Text Data ◽  
Image Domain ◽  
Semantic Learning ◽  
The Web

Image mining is more than just an extension of data mining to image domain. Web Image mining is a technique commonly used to extract knowledge directly from images on WWW. Since main targets of conventional Web mining are numerical and textual data, Web mining for image data is on demand. There are huge image data as well as text data on the Web. However, mining image data from the Web is paid less attention than mining text data, since treating semantics of images are much more difficult. This paper proposes a novel image recognition and image classification technique using a large number of images automatically gathered from the Web as learning images. For classification the system uses imagefeature- based search exploited in content-based image retrieval(CBIR), which do not restrict target images unlike conventional image recognition methods and support vector machine(SVM), which is one of the most efficient & widely used statistical method for generic image classification that fit to the learning tasks. By the experiments it is observed that the proposed system outperforms some existing search systems.

Fuzzy Support Vector Machine for PolSAR Image Classification

2013 ◽  
Vol 639-640 ◽  
pp. 1162-1167 ◽  
Author(s):  
Hong Xia Ke ◽  
Guo Dong Liu ◽  
Guo Bing Pan
Keyword(s):  
Support Vector Machine ◽  
Image Classification ◽  
Image Data ◽  
Original Data ◽  
Total Power ◽  
Support Vector ◽  
Fuzzy Support Vector Machine ◽  
Polarimetric Synthetic Aperture Radar ◽  
Automatic Categorization ◽  
Svm Algorithm

Fully Polarimetric Synthetic Aperture Radar (PolSAR) image classification, with the complexity for its data’s scattering mechanism and statistical property, has expected to be performed by an automatic categorization. This paper presents a supervised method called Fuzzy support vector machine (FSVM), which is a variant of the SVM algorithm to classify the PolSAR image data. In order to take advantages of PolSAR data, five scattering features (entropy, total power, three Eigenvalues of Coherent Matrix: λ1,λ2,λ3) are input as original data space of the FSVM algorithm. The feasibility of this approach is examined by the JPL/AIRSAR PolSAR data. The classification results show that the proposed FSVM method has out-performed the SVM method.

scholarly journals Multiscale filter-based hyperspectral image classification with PCA and SVM

2021 ◽  
Vol 72 (1) ◽  
pp. 40-45
Author(s):  
Guang Yi Chen
Keyword(s):  
Image Classification ◽  
Hyperspectral Image ◽  
Image Data ◽  
Principal Component ◽  
Data Cube ◽  
High Spectral Resolution ◽  
Computational Time ◽  
Support Vector ◽  
Hyperspectral Image Classification ◽  
Hyperspectral Image Data

Abstract Hyperspectral imagery can offer images with high spectral resolution and provide a unique ability to distinguish the subtle spectral signatures of different land covers. In this paper, we develop a new algorithm for hyperspectral image classification by using principal component analysis (PCA) and support vector machines (SVM). We use PCA to reduce the dimensionality of an HSI data cube, and then perform spatial convolution with three different filters on the PCA output cube. We feed all three convolved output cubes to SVM to classify every pixel. Finally, we perform fusion on the three output maps to determine the final classification map. We conduct experiments on three widely used hyperspectral image data cubes (ie indian pines, pavia university, and salinas). Our method can improve the classification accuracy significantly when compared to several existing methods. Our novel method is relatively fast in term of CPU computational time as well.

scholarly journals Comparative Analysis of Image Classification Algorithms for Face Mask Detection

2021 ◽  
Vol 7 (1) ◽  
pp. 56
Author(s):  
Mohammad Farid Naufal ◽  
Selvia Ferdiana Kusuma ◽  
Zefanya Ardya Prayuska ◽  
Ang Alexander Yoshua ◽  
Yohanes Albert Lauwoto ◽  
...  
Keyword(s):  
Image Classification ◽  
Learning Algorithm ◽  
Image Data ◽  
Face Mask ◽  
Classification Model ◽  
Support Vector ◽  
Classification Algorithms ◽  
Alert System ◽  
K Nearest Neighbors ◽  
Deep Learning Algorithm

Background: The COVID-19 pandemic remains a problem in 2021. Health protocols are needed to prevent the spread, including wearing a face mask. Enforcing people to wear face masks is tiring. AI can be used to classify images for face mask detection. There are a lot of image classification algorithm for face mask detection, but there are still no studies that compare their performance.Objective: This study aims to compare the classification algorithms of classical machine learning. They are k-nearest neighbors (KNN), support vector machine (SVM), and a widely used deep learning algorithm for image classification which is convolutional neural network (CNN) for face masks detection.Methods: This study uses 5 and 3 cross-validation for assessing the performance of KNN, SVM, and CNN in face mask detection.Results: CNN has the best average performance with the accuracy of 0.9683 and average execution time of 2,507.802 seconds for classifying 3,725 faces with mask and 3,828 faces without mask images.Conclusion: For a large amount of image data, KNN and SVM can be used as temporary algorithms in face mask detection due to their faster execution times. At the same time, CNN can be trained to form a classification model. In this case, it is advisable to use CNN for classification because it has better performance than KNN and SVM. In the future, the classification model can be implemented for automatic alert system to detect and warn people who are not wearing face masks.  

Cross-Modality Transfer Learning for Image-Text Information Management

2022 ◽  
Vol 13 (1) ◽  
pp. 1-14
Author(s):  
Shuteng Niu ◽  
Yushan Jiang ◽  
Bowen Chen ◽  
Jian Wang ◽  
Yongxin Liu ◽  
...  
Keyword(s):  
Image Classification ◽  
Information Management ◽  
Transfer Learning ◽  
Information Transfer ◽  
Image Data ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Classification Problems ◽  
Target Domain ◽  
Text Data

In the past decades, information from all kinds of data has been on a rapid increase. With state-of-the-art performance, machine learning algorithms have been beneficial for information management. However, insufficient supervised training data is still an adversity in many real-world applications. Therefore, transfer learning (TF) was proposed to address this issue. This article studies a not well investigated but important TL problem termed cross-modality transfer learning (CMTL). This topic is closely related to distant domain transfer learning (DDTL) and negative transfer. In general, conventional TL disciplines assume that the source domain and the target domain are in the same modality. DDTL aims to make efficient transfers even when the domains or the tasks are entirely different. As an extension of DDTL, CMTL aims to make efficient transfers between two different data modalities, such as from image to text. As the main focus of this study, we aim to improve the performance of image classification by transferring knowledge from text data. Previously, a few CMTL algorithms were proposed to deal with image classification problems. However, most existing algorithms are very task specific, and they are unstable on convergence. There are four main contributions in this study. First, we propose a novel heterogeneous CMTL algorithm, which requires only a tiny set of unlabeled target data and labeled source data with associate text tags. Second, we introduce a latent semantic information extraction method to connect the information learned from the image data and the text data. Third, the proposed method can effectively handle the information transfer across different modalities (text-image). Fourth, we examined our algorithm on a public dataset, Office-31. It has achieved up to 5% higher classification accuracy than “non-transfer” algorithms and up to 9% higher than existing CMTL algorithms.

Mammogram Mining Using Genetic Ant-Miner

2011 ◽  
pp. 232-250
Author(s):  
K. Thangavel ◽  
R. Roselin
Keyword(s):  
Data Mining ◽  
Genetic Algorithm ◽  
Image Analysis ◽  
Comparative Analysis ◽  
Image Data ◽  
Experimental Results ◽  
Biomedical Image Analysis ◽  
Image Mining ◽  
Image Domain ◽  
Biomedical Image

Image mining deals with the extraction of implicit knowledge, image data relationship, or other patterns not explicitly stored in the images. It is an extension of data mining to image domain and an interdisciplinary endeavour. This chapter focuses on mammogram classification using genetic Ant-Miner. The key idea is to generate classifier for classifying mammograms as normal or abnormal using the proposed Genetic Ant-Miner algorithm. The Genetic Algorithm has been employed to optimize some of the ant parameters. A comparative analysis is performed in order to achieve the efficiency of the proposed algorithm. Further, the experimental results reveals that the improvement of the proposed Genetic Ant-Miner in the domain of Biomedical image Analysis.

Information System in Image Classification Based on SVM and Color Clustering Analysis

2014 ◽  
Vol 886 ◽  
pp. 572-575
Author(s):  
Li Ying Qi ◽  
Ke Gang Wang
Keyword(s):  
Support Vector Machine ◽  
Image Classification ◽  
Spatial Information ◽  
Basic Research ◽  
Color Histogram ◽  
Support Vector ◽  
Color Information ◽  
Machine Model ◽  
Color Feature ◽  
Image Domain

Effective use of the color feature of Content Based Image Retrieval (CBIR) and Image classification is an important basic research, but there are some shortcomings in the color histogram representation method, such as high dimension, pixels spatial information is ignored and so on. Although color feature data can reduce the dimension by quantification, but some useful image color information will be discard. In this paper, the image color information processing in space constrained fuzzy clustering to obtain a lower dimensional color feature data of the image characteristics of domain colors description, and use multi-class support vector machine to classify color images. Experimental results show that the proposed method can better describe image color information than color histogram; image domain color description combined with support vector machine model can achieve the automatic classification of images effectively.

scholarly journals Web Data Classification Using Support Vector Machine Back Propagation Neural Network

2019 ◽  
Vol 8 (3) ◽  
pp. 5446-5448
Keyword(s):  
Web Mining ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Support Vector ◽  
Web Content ◽  
Web Content Mining ◽  
Content Mining ◽  
Time Calculation ◽  
Content Information ◽  
The Web

These days, the development of World Wide Web has surpassed a lot with extra desires. Extraordinary arrangement of content reports, transmission records and pictures were reachable inside the web it's as yet expanding in its structures. Information handling is that the style of removing information's realistic inside the web. Web mining could be a piece of information preparing that identifies with differed examination networks like data recovery, bearing frameworks and artificial insight. The data's in these structures are very much organized from the beginning. This web mining receives a great deal of the date mining procedures to discover most likely supportive data from web substance. The ideas of web mining with its classifications were examined. The paper chiefly focused on the web Content mining undertakings along the edge of its procedures and calculations. In this paper we proposed AI calculation based order .SVM_BPM calculation grouped the web content information and thought about existing calculations our proposed arrangement calculation is high effective and less time calculation

scholarly journals Closing the Performance Gap between Siamese Networks for Dissimilarity Image Classification and Convolutional Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5809
Author(s):  
Loris Nanni ◽  
Giovanni Minchio ◽  
Sheryl Brahnam ◽  
Davide Sarraggiotto ◽  
Alessandra Lumini
Keyword(s):  
Image Classification ◽  
Image Data ◽  
Support Vector ◽  
Final Decision ◽  
Data Sets ◽  
Data Set ◽  
Additional Information ◽  
New Strategy ◽  
Fully Connected ◽  
Siamese Networks

In this paper, we examine two strategies for boosting the performance of ensembles of Siamese networks (SNNs) for image classification using two loss functions (Triplet and Binary Cross Entropy) and two methods for building the dissimilarity spaces (FULLY and DEEPER). With FULLY, the distance between a pattern and a prototype is calculated by comparing two images using the fully connected layer of the Siamese network. With DEEPER, each pattern is described using a deeper layer combined with dimensionality reduction. The basic design of the SNNs takes advantage of supervised k-means clustering for building the dissimilarity spaces that train a set of support vector machines, which are then combined by sum rule for a final decision. The robustness and versatility of this approach are demonstrated on several cross-domain image data sets, including a portrait data set, two bioimage and two animal vocalization data sets. Results show that the strategies employed in this work to increase the performance of dissimilarity image classification using SNN are closing the gap with standalone CNNs. Moreover, when our best system is combined with an ensemble of CNNs, the resulting performance is superior to an ensemble of CNNs, demonstrating that our new strategy is extracting additional information.

Classifying Web Pages by Aimed Nation Using Machine Learning

2017 ◽  
Vol 7 (1) ◽  
pp. 20-35 ◽  
Author(s):  
Boudheb Tarik ◽  
Djelloul Daouadji Mahmoud ◽  
Elberrichi Zakaria
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Web Mining ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Experimental Result ◽  
Support Vector ◽  
Web Pages ◽  
Original Application ◽  
The Web

Classifying web pages is to automatically assign predefined class to them. It is one of the main applications of web mining. The authors' aim is to detect the targeted nation based on the web pages content. It is an original application. In this paper, the authors propose different web mining approaches using machine learning algorithms such as Naïve Bayes and Support Vector Machine in order classify them. They present detailed stages of the procedure. The best experimental result based on an original corpus created by their own means shows a very attention grabbing f-score of 85%.

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