scholarly journals An Optimized Approach for Image Segmentation on Mobile Devices

2021 ◽  
Vol 12 (2) ◽  
pp. 3018-3024
Author(s):  
Hemantkumar R. Turkar, Et. al.
Keyword(s):  
Global Optimization ◽  
Image Segmentation ◽  
Clustering Algorithms ◽  
Smart Devices ◽  
Qualitative And Quantitative Analysis ◽  
Data Set ◽  
Qualitative And Quantitative ◽  
Standard Data ◽  
Quad Tree ◽  
Strong Ability

Now a day’s image segmentation is widely used in many multimedia applications. We have introduced the optimized approach for image segmentation based on clustering for use on smart devices. The proposed optimized approach is based on the combination of partitioning of images using quad-tree and Ant Colony Optimization. This approach utilizes the strong ability of ACO i.e global optimization. The proposed optimized algorithm is evaluated on images of standard data set and its performance is compared with existing clustering algorithms. The qualitative and quantitative analysis has been performed to measure the efficacy of the optimized approach over conventional existing algorithms. This procedure obtains better quality results than existing clustering algorithms.

scholarly journals Bees Assorter

2020 ◽  
Vol 6 (12) ◽  
pp. 61-65
Author(s):  
Himanshu Verma
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Bumble Bee ◽  
Wing Size ◽  
Qualitative And Quantitative Analysis ◽  
Data Set ◽  
Qualitative And Quantitative ◽  
The Difference

Many attempts were made to classify the bees that is bumble bee or honey bee , there have been such a large amount of researches which were made to seek out the difference between them on the premise of various features like wing size , size of bee , color, life cycle and many more. But altogether the analysis there have been either that specialize in qualitative or quantitative , but to beat this issue , thus researchers came up with an answer which might be both qualitative and quantitative analysis made to classify them. And making use of machine learning algorithm to classify them gives a lift . Now the classification would take less time as these algorithms are pretty fast and accurate . By using machine learning work is made easy . Lots of photographs had to be collected and stored for data set. And by using these machine learning algorithms we would be getting information about the bees which might be employed by researchers in further classification of bees. Manipulation of images had to be done so as on prepare them in such a way that they will be applied to the algorithms and have feature extraction done. As there have been a lot of photographs(data set) which take a lot of space and also the area in which bees were present in these photographs were too small so to accommodate it dimension reduction was done , it might not consider other images like trees , leaves , flowers which were there present in the photograph which we elect as a data set.

scholarly journals “The Curse of the Caribbean”? Agency’s Impact on the Productivity of Sugar Estates on St. Vincent and the Grenadines, 1814–1829

2018 ◽  
Vol 78 (2) ◽  
pp. 472-499 ◽  
Author(s):  
S. D. Smith ◽  
Martin Forster
Keyword(s):  
Quantitative Analysis ◽  
Fixed Effects ◽  
Qualitative And Quantitative Analysis ◽  
Early Nineteenth Century ◽  
Data Set ◽  
Qualitative And Quantitative ◽  
Sugar Plantation ◽  
Fixed Effects Models ◽  
The Caribbean ◽  
Atlantic Economy

This study estimates agency’s impact on sugar plantation productivity using a unique early nineteenth-century panel data set from St. Vincent and the Grenadines. Results of fixed effects models, combined with a qualitative and quantitative analysis of potential endogeneity of the agency variable, provide no evidence that estates managed by agents were less productive than those managed by their owners. We discuss the results in the context of the historical and recent, revisionary, interpretations of agency, and the emergence of managerial hierarchies in the Atlantic economy.

scholarly journals Image Processing of SEM Image Nano Silver Using K-means MATLAB Technique

2019 ◽  
Vol 29 (3) ◽  
pp. 150 ◽  
Author(s):  
Elham Jasim Mohammad
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Clustering Algorithms ◽  
Small Data ◽  
Data Sets ◽  
Data Set ◽  
Sem Image ◽  
Huge Data ◽  
And Performance ◽  
Biometric Information

Nanotechnology is one of the non-exhaustive applications in which image processing is used. For optimal nanoparticle visualization and characterization, the high resolution Scanning Electron Microscope (SEM) and the Atomic Force Microscope (AFM) are used. Image segmentation is one of the critical steps in nanoscale processing. There are also different ways to reach retail, including statistical approximations.In this study; we used the K-means method to determine the optimal threshold using statistical approximation. This technique is thoroughly studied for the SEM nanostructure Silver image. Note that, the image obtained by SEM is good enough to analyze more recently images. The analysis is being used in the field of nanotechnology. The K-means algorithm classifies the data set given to k groups based on certain measurements of certain distances. K-means technology is the most widely used among all clustering algorithms. It is one of the common techniques used in statistical data analysis, image analysis, neural networks, classification analysis and biometric information. K-means is one of the fastest collection algorithms and can be easily used in image segmentation. The results showed that K-means is highly sensitive to small data sets and performance can degrade at any time. When exposed to a huge data set such as 100.000, the performance increases significantly. The algorithm also works well when the number of clusters is small. This technology has helped to provide a good performance algorithm for the state of the image being tested.

Underwater Image Segmentation Using Human Psycho Visual Phenomenon

2018 ◽  
pp. 130-146 ◽  
Author(s):  
Soumyadip Dhar ◽  
Hiranmoy Roy
Keyword(s):  
Image Segmentation ◽  
Wavelet Packet ◽  
Data Set ◽  
Standard Data ◽  
Underwater Image ◽  
Large Numbers ◽  
Visual Technique ◽  
Visual Phenomenon ◽  
Novel Method ◽  
Adaptive Basis Selection

In this chapter, a novel method is proposed for underwater image segmentation based on human psycho visual phenomenon (HVS). In the proposed method the texture property of an image is captured by decomposing it into frequency sub-bands using M-band wavelet packet transform. The sub-bands represent the image in different scales and orientations. The large numbers of sub-bands are pruned by an adaptive basis selection. The proper sub-bands for segmentation are selected depending on the HVS. The HVS imitates the original visual technique of a human being and it is used to divide each sub-band in Weber, De-Vries Rose, and saturation regions. A wavelet packet sub-band is selected for segmentation depending on those three regions. The performance of the proposed method is found to be superior to that of the state-of-the-art methods for underwater image segmentation on standard data set.

CLUSTERING ALGORITHM RESEARCH BASED ON SELF-ORGANIZING FEATURE MAPS NETWORKS

2006 ◽  
Vol 20 (07) ◽  
pp. 985-1000 ◽  
Author(s):  
JUNHAO WEN ◽  
HONGYAN WU ◽  
ZHONGFU WU ◽  
YUANYAN TANG ◽  
GUANGHUI HE
Keyword(s):  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Feature Maps ◽  
Data Set ◽  
Topological Similarity ◽  
Standard Data ◽  
Data Space ◽  
New Concepts ◽  
Different Characteristics ◽  
Self Organizing

Self-organizing feature maps (SOFM) can learn both the distribution and topology of the input vectors they are trained on. According to this characteristic, we construct neural networks with a family of self-organizing feature maps to cluster the input data space. The proposed algorithm in this paper defines a novel similarity measure, topological similarity, and employs some new concepts, such as SOFM family, UsageFactor. The clustering algorithm handles the clusters with arbitrary shapes and avoid the limitations of the conventional clustering algorithms. We conclude our paper by several experiments with synthetic and standard data set of different characteristics, which show good performance of the proposed algorithm.

scholarly journals Research on K-Value Selection Method of K-Means Clustering Algorithm

J ◽  
2019 ◽  
Vol 2 (2) ◽  
pp. 226-235 ◽  
Author(s):  
Chunhui Yuan ◽  
Haitao Yang
Keyword(s):  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Simple Algorithm ◽  
Convergence Result ◽  
Data Set ◽  
K Value ◽  
Standard Data ◽  
Advantages And Disadvantages ◽  
Gap Statistic ◽  
Selection Algorithms

Among many clustering algorithms, the K-means clustering algorithm is widely used because of its simple algorithm and fast convergence. However, the K-value of clustering needs to be given in advance and the choice of K-value directly affect the convergence result. To solve this problem, we mainly analyze four K-value selection algorithms, namely Elbow Method, Gap Statistic, Silhouette Coefficient, and Canopy; give the pseudo code of the algorithm; and use the standard data set Iris for experimental verification. Finally, the verification results are evaluated, the advantages and disadvantages of the above four algorithms in a K-value selection are given, and the clustering range of the data set is pointed out.

A qualitative–quantitative study of science mapping by different algorithms: The Polish journals landscape

2020 ◽  
pp. 016555152090273
Author(s):  
Veslava Osinska
Keyword(s):  
Clustering Algorithms ◽  
Science Mapping ◽  
Data Set ◽  
Qualitative And Quantitative ◽  
Knowledge Domains ◽  
Clustering Quality ◽  
Information Studies ◽  
Quantitative And Qualitative Methods ◽  
Local Proximity

By applying different clustering algorithms, the author strived to construct the best visual representation of scientific domains and disciplines in Poland. Journals and their disciplinary categories constituted a data set. A comparative analysis of maps was based on both qualitative and quantitative approaches. Complex patterns of eight maps were evaluated taking into account both the local proximity of disciplines and the whole structure of presented domains. Final clustering quality value was introduced and calculated in reference to the knowledge domains. The authors underlined the role of quantitative and qualitative methods in combination in the mapping evaluation. The best results were obtained with the T-distributed stochastic neighbour embedding (t-SNE) algorithm. This youngest technique may have the biggest potential for semantic information studies and in the scope of broadly understood semantic solutions.

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