scholarly journals THE LOW BACKSCATTERING OBJECTS CLASSIFICATION IN POLSAR IMAGE BASED ON BAG OF WORDS MODEL USING SUPPORT VECTOR MACHINE

2018 ◽  
Vol XLII-3 ◽  
pp. 2089-2091
Author(s):  
L. Yang ◽  
L. Shi ◽  
P. Li ◽  
J. Yang ◽  
L. Zhao ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Bare Soil ◽  
Radar Signal ◽  
Support Vector ◽  
Data Sets ◽  
Bag Of Words ◽  
Scale Invariant ◽  
Polarimetric Synthetic Aperture Radar ◽  
Sift Features ◽  
Scale Invariant Feature

Due to the forward scattering and block of radar signal, the water, bare soil, shadow, named low backscattering objects (LBOs), often present low backscattering intensity in polarimetric synthetic aperture radar (PolSAR) image. Because the LBOs rise similar backscattering intensity and polarimetric responses, the spectral-based classifiers are inefficient to deal with LBO classification, such as Wishart method. Although some polarimetric features had been exploited to relieve the confusion phenomenon, the backscattering features are still found unstable when the system noise floor varies in the range direction. This paper will introduce a simple but effective scene classification method based on Bag of Words (BoW) model using Support Vector Machine (SVM) to discriminate the LBOs, without relying on any polarimetric features. In the proposed approach, square windows are firstly opened around the LBOs adaptively to determine the scene images, and then the Scale-Invariant Feature Transform (SIFT) points are detected in training and test scenes. The several SIFT features detected are clustered using K-means to obtain certain cluster centers as the visual word lists and scene images are represented using word frequency. At last, the SVM is selected for training and predicting new scenes as some kind of LBOs. The proposed method is executed over two AIRSAR data sets at C band and L band, including water, bare soil and shadow scenes. The experimental results illustrate the effectiveness of the scene method in distinguishing LBOs.

scholarly journals SURF Points Versus SIFT Points in Identification of Medicinal Plants

2019 ◽  
Vol 9 (2) ◽  
pp. 602-607
Keyword(s):  
Image Processing ◽  
Support Vector Machine ◽  
Medicinal Plants ◽  
Digital Image Processing ◽  
Digital Image ◽  
Support Vector ◽  
Scale Invariant ◽  
Invariant Feature ◽  
Feature Transform ◽  
Scale Invariant Feature

Today, digital image processing is used in diverse fields; this paper attempts to compare the outcome of two commonly used techniques namely Speeded Up Robust Feature (SURF) points and Scale Invariant Feature Transform (SIFT) points in image processing operations. This study focuses on leaf veins for identification of plants. An algorithm sequence has been utilized for the purpose of recognition of leaves. SURF and SIFT extractions are applied to define and distinguish the limited structures of the documented vein image of the leaf separately and Support Vector Machine (SVM) is integrated to classify and identify the correct plant. The results prove that the SURF algorithm is the fastest and an efficient one. The results of the study can be extrapolated to authenticate medicinal plants which is the starting step to standardize herbs and carryout research.

scholarly journals Sasirangan Motifs Classification using Scale- Invariant Feature Transform (SIFT) and Support Vector Machine (SVM)

2019 ◽  
Vol 280 ◽  
pp. 05023
Author(s):  
Muhammad Alkaff ◽  
Husnul Khatimi ◽  
Nur Lathifah ◽  
Yuslena Sari
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Scale Invariant Feature Transform ◽  
Primary Data ◽  
Support Vector ◽  
Scale Invariant ◽  
Svm Classification ◽  
Invariant Feature ◽  
Feature Transform ◽  
Scale Invariant Feature

Sasirangan is one of the traditional cloth from Indonesia. Specifically, it comes from South Borneo. It has many variations of motifs with a different meaning for each pattern. This paper proposes a prototype of Sasirangan motifs classification using four (4) type of Sasirangan motifs namely Hiris Gagatas, Gigi Haruan, Kulat Kurikit, and Hiris Pudak. We used primary data of Sasirangan images collected from Kampung Sasirangan, Banjarmasin, South Kalimantan. After that, the images are processed using Scale-Invariant Feature Transform (SIFT) to extract its features. Furthermore, the extracted features vectors obtained is classified using the Support Vector Machine (SVM). The result shows that the Scale- Invariant Feature Transform (SIFT) feature extraction with Support Vector Machine (SVM) classification able to classify Sasirangan motifs with an overall accuracy of 95%.

Support Vector Machines for Dyadic Data

2006 ◽  
Vol 18 (6) ◽  
pp. 1472-1510 ◽  
Author(s):  
Sepp Hochreiter ◽  
Klaus Obermayer
Keyword(s):  
Support Vector Machine ◽  
Feature Selection ◽  
New Technique ◽  
Support Vector ◽  
Data Sets ◽  
Real World Data ◽  
Scale Invariant ◽  
Dyadic Data ◽  
Regression Functions ◽  
Classification And Regression

We describe a new technique for the analysis of dyadic data, where two sets of objects (row and column objects) are characterized by a matrix of numerical values that describe their mutual relationships. The new technique, called potential support vector machine (P-SVM), is a large-margin method for the construction of classifiers and regression functions for the column objects. Contrary to standard support vector machine approaches, the P-SVM minimizes a scale-invariant capacity measure and requires a new set of constraints. As a result, the P-SVM method leads to a usually sparse expansion of the classification and regression functions in terms of the row rather than the column objects and can handle data and kernel matrices that are neither positive definite nor square. We then describe two complementary regularization schemes. The first scheme improves generalization performance for classification and regression tasks; the second scheme leads to the selection of a small, informative set of row support objects and can be applied to feature selection. Benchmarks for classification, regression, and feature selection tasks are performed with toy data as well as with several real-world data sets. The results show that the new method is at least competitive with but often performs better than the benchmarked standard methods for standard vectorial as well as true dyadic data sets. In addition, a theoretical justification is provided for the new approach.

Scene Categorization Through Combining LBP and SIFT Features Effectively

2015 ◽  
Vol 30 (01) ◽  
pp. 1655001 ◽  
Author(s):  
Shung Bai ◽  
Jianjun Hou ◽  
Noboru Ohnishi
Keyword(s):  
Input Image ◽  
Support Vector ◽  
Two Dimensional ◽  
Scale Invariant ◽  
Scene Categorization ◽  
Visual Words ◽  
Training Images ◽  
Sift Features ◽  
Image Representations ◽  
Scale Invariant Feature

In computer vision, Local Binary Pattern (LBP) and Scale Invariant Feature Transform (SIFT) are two widely used local descriptors. In this paper, we propose to combine them effectively for scene categorization. First, LBP and SIFT features are regularly extracted from training images for constructing a LBP feature codebook and a SIFT feature codebook. Then, a two-dimensional table is created by combining the obtained codebooks. For creating a representation for an image, LBP and SIFT features extracted from the same positions of the image are encoded together based on sparse coding by using the two-dimensional table. After processing all features in the input image, we adopt spatial max pooling to determine its representation. Obtained image representations are forwarded to a Support Vector Machine classifier for categorization. In addition, in order to improve the scene categorization performance further, we propose a method to select correlated visual words from large codebooks for constructing the two-dimensional table. Finally, for evaluating the proposed method, extensive experiments are implemented on datasets Scene Categories 8, Scene Categories 15 and MIT 67 Indoor Scene. It is demonstrated that the proposed method is effective for scene categorization.

The Automatic Image Registration Algorithm Based on Bag of Words and Features Point

2014 ◽  
Vol 1049-1050 ◽  
pp. 1599-1602
Author(s):  
Ying Hou ◽  
Gui Cai Wang
Keyword(s):  
Support Vector Machine ◽  
Image Registration ◽  
Clustering Algorithm ◽  
Support Vector ◽  
Bag Of Words ◽  
Registration Algorithm ◽  
Image Registration Algorithm ◽  
Sift Features

According to actual requirements and situation, a novel automatic image registration algorithm was presented based on bag of words and features point. Firstly, pyramid delaminating was used to preprocess the pre-registration images. Then, image normalized could be obtained. Secondly, the dense SIFT features points could be extracted. The paper could get the feature eigenvectors descriptions of image by k-means clustering algorithm. Thirdly, the feature eigenvectors descriptions of image were train and the reference and pre-registration images were classification through Support Vector Machine (SVM). Finally, image registration could implement by features matching points. The results of experiment with the with office room and building test images shown the proposed algorithm was effective and could obtain the better registration image.

scholarly journals Support Vector Machine Based Feature Extraction for Gender Recognition From Objects Using Lasso Classifier

2020 ◽  
Author(s):  
Damodara Krishna Kishore Galla ◽  
BabuReddy Mukamalla ◽  
Rama Prakasha Reddy Chegireddy
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Support Vector ◽  
Gender Recognition ◽  
Classification Models ◽  
Lasso Regression ◽  
Scale Invariant ◽  
And Gender ◽  
Blind Persons ◽  
Scale Invariant Feature

Abstract Object detection and gender recognition are the two different categories to be classified in a single section is a complicated task and needs to support the blind people.In this paper our method to better sensation of a blind persons by conversion of visualized data to audio data.Therefore the artificial intelligence model requires to detect the objects as well as human face recognition with gender classification algorithms. This model processed with feature extraction and classification models. The feature extraction was comprised with multi scale invariant feature transform(MSIFT), with feature optimization with support vector machine algorithm then classified using LASSO classifier. For better performance identification three different classification models were implemented and tested too. Feature selection helps in making tests early to detect the objects and recognising human actions using image processing approach. This can be applied for both offline and online modes. But in this scenario offline mode was implemented and was tested with combination of different databases. For this process of classification ridge regression (RR), elastic net (EN), lasso regression(LR) and lasso regression were implemented.The final classfication results with accuracy are as follows for RR- 89.6%, EN- 93.5%, LR-93.2% and propsed approach(LRGS) with 98.4% accurate detection rate with prediction name of classes.

scholarly journals A One-shot Learning Approach to Image Classification using Genetic Programming

2020 ◽  
Author(s):  
Harith Al-Sahaf ◽  
Mengjie Zhang ◽  
M Johnston
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Genetic Programming ◽  
Image Classification ◽  
Local Binary Patterns ◽  
Support Vector ◽  
Learning Approach ◽  
Data Sets ◽  
Domain Specific ◽  
International Publishing

In machine learning, it is common to require a large number of instances to train a model for classification. In many cases, it is hard or expensive to acquire a large number of instances. In this paper, we propose a novel genetic programming (GP) based method to the problem of automatic image classification via adopting a one-shot learning approach. The proposed method relies on the combination of GP and Local Binary Patterns (LBP) techniques to detect a predefined number of informative regions that aim at maximising the between-class scatter and minimising the within-class scatter. Moreover, the proposed method uses only two instances of each class to evolve a classifier. To test the effectiveness of the proposed method, four different texture data sets are used and the performance is compared against two other GP-based methods namely Conventional GP and Two-tier GP. The experiments revealed that the proposed method outperforms these two methods on all the data sets. Moreover, a better performance has been achieved by Naïve Bayes, Support Vector Machine, and Decision Trees (J48) methods when extracted features by the proposed method have been used compared to the use of domain-specific and Two-tier GP extracted features. © Springer International Publishing 2013.

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