Watermelon Quality Classification Using Weighted K-means Algortihm Based On Feautures YCbCr Color

the classification of fruit quality on a computer using image data is very necessary. In addition, this can also be used in making decisions and policies related to business strategies in the industry. In this research, the quality classification of watermelon was carried out using the Weighted K-Means Algorithm. The classification of watermelon fruit in this study was divided into three groups, namely fresh, medium, and rotten. The classification process in the system created is divided into two stages, namely training and examinations.The data that is input into the system is watermelon image data in YCbCr format. In the training phase, the input data that is processed is image data that has been classified. As for the testing/classification phase, the input data processed is an arbitrary image that has not been classified.The results of the classification with watermelon case studies using the weighted k-means algorithm obtained a conclusion that the greater the amount of training data, the computing time needed for the training and testing process will increase, as well as the level of accuracy, precision and recall of the classification results obtained will also get better. While the greater the number of k values, the computational time needed for the training and testing process will increase, but the level of accuracy, precision, and recall of the results of the classification that gets smaller.

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Business Intelligence using the K-Nearest Neighbor Algorithm to Analyze Customer Behavior in Online Crowdfunding Systems

E3S Web of Conferences ◽

10.1051/e3sconf/202020216005 ◽

2020 ◽

Vol 202 ◽

pp. 16005

Author(s):

Chashif Syadzali ◽

Suryono Suryono ◽

Jatmiko Endro Suseno

Keyword(s):

Business Intelligence ◽

Nearest Neighbor ◽

Customer Behavior ◽

Training Data ◽

Business Strategies ◽

Intelligence Analysis ◽

K Nearest Neighbor ◽

Nearest Neighbor Algorithm ◽

K Nearest Neighbor Algorithm

Customer behavior classification can be useful to assist companies in conducting business intelligence analysis. Data mining techniques can classify customer behavior using the K-Nearest Neighbor algorithm based on the customer's life cycle consisting of prospect, responder, active and former. Data used to classify include age, gender, number of donations, donation retention and number of user visits. The calculation results from 2,114 data in the classification of each customer’s category are namely active by 1.18%, prospect by 8.99%, responder by 4.26% and former by 85.57%. System accuracy using a range of K from K = 1 to K = 20 produces that the highest accuracy is 94.3731% at a value of K = 4. The results of the training data that produce a classification of user behavior can be used as a Business Intelligence analysis that is useful for companies in determining business strategies by knowing the target of optimal market.

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EFFICIENT CLASSIFICATION OF SCANNED MEDIA USING SPATIAL STATISTICS

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001410008263 ◽

2010 ◽

Vol 24 (06) ◽

pp. 917-946

Author(s):

GOZDE UNAL ◽

GAURAV SHARMA ◽

REINER ESCHBACH

Keyword(s):

Spatial Statistics ◽

High Reliability ◽

Image Data ◽

Training Data ◽

Automated Classification ◽

Data Set ◽

Statistical Measures ◽

Scanned Image ◽

Scanned Images

Photography, lithography, xerography, and inkjet printing are the dominant technologies for color printing. Images produced on these "different media" are often scanned either for the purpose of copying or creating an electronic representation. For an improved color calibration during scanning, a media identification from the scanned image data is desirable. In this paper, we propose an efficient algorithm for automated classification of input media into four major classes corresponding to photographic, lithographic, xerographic and inkjet. Our technique exploits the strong correlation between the type of input media and the spatial statistics of corresponding images, which are observed in the scanned images. We adopt ideas from spatial statistics literature, and design two spatial statistical measures of dispersion and periodicity, which are computed over spatial point patterns generated from blocks of the scanned image, and whose distributions provide the features for making a decision. We utilize extensive training data and determined well separated decision regions to classify the input media. We validate and tested our classification technique results over an independent extensive data set. The results demonstrate that the proposed method is able to distinguish between the different media with high reliability.

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Automatic classification of ovarian cancer types from cytological images using deep convolutional neural networks

Bioscience Reports ◽

10.1042/bsr20180289 ◽

2018 ◽

Vol 38 (3) ◽

Cited By ~ 11

Author(s):

Miao Wu ◽

Chuanbo Yan ◽

Huiqiang Liu ◽

Qian Liu

Keyword(s):

Neural Networks ◽

Ovarian Cancer ◽

Convolutional Neural Networks ◽

Image Data ◽

Training Data ◽

Serous Carcinoma ◽

Deep Convolutional Neural Networks ◽

Ovarian Cancers ◽

Cancer Types

Ovarian cancer is one of the most common gynecologic malignancies. Accurate classification of ovarian cancer types (serous carcinoma, mucous carcinoma, endometrioid carcinoma, transparent cell carcinoma) is an essential part in the different diagnosis. Computer-aided diagnosis (CADx) can provide useful advice for pathologists to determine the diagnosis correctly. In our study, we employed a Deep Convolutional Neural Networks (DCNN) based on AlexNet to automatically classify the different types of ovarian cancers from cytological images. The DCNN consists of five convolutional layers, three max pooling layers, and two full reconnect layers. Then we trained the model by two group input data separately, one was original image data and the other one was augmented image data including image enhancement and image rotation. The testing results are obtained by the method of 10-fold cross-validation, showing that the accuracy of classification models has been improved from 72.76 to 78.20% by using augmented images as training data. The developed scheme was useful for classifying ovarian cancers from cytological images.

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Quality Classification of Dragon Fruits Based on External Performance Using a Convolutional Neural Network

Applied Sciences ◽

10.3390/app112210558 ◽

2021 ◽

Vol 11 (22) ◽

pp. 10558

Author(s):

Nguyen Minh Trieu ◽

Nguyen Truong Thinh

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Processing Speed ◽

High Efficiency ◽

Processing System ◽

Training Data ◽

Labor Costs ◽

Quality Classification ◽

Dragon Fruit

Currently, most agricultural products in developing countries are exported to many countries around the world. Therefore, the classification of these products according to different standards is necessary. In Vietnam, dragon fruit is considered as the fruit with the highest export rate. Currently, the classification of dragon fruit is carried manually, lead to low-quality classification high labor costs. Therefore, this study describes an automatic dragon fruit classifying system using non-destructive measurements, based on a convolutional neural network (CNN). This classifying system uses a combination of a model of machine learning and image processing using a convolutional neural network to identify the external features of dragon fruits; the fruits are then classified and evaluated by groups. The dragon fruit is recognized by the system, which extracts the objects combined with the signal obtained from the loadcell to calculate and determine dragon fruit in each group. The training data are collected from the dragon fruit processing system, with a dataset of images obtained from more than 1287 dragon fruits, to train the model. In this system, the classification of the processing speed and accuracy are the two most important factors. The results show that the classification system achieves high efficiency. The system is effective with existing dragon fruit types. In Vietnamese factories, the processing speed of the system increases the sorting capacity of export packing facilities to six times higher than that of the manual method, with an accuracy of more than 96%.

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CLASSIFICATION UNDER LABEL NOISE BASED ON OUTDATED MAPS

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-iv-1-w1-215-2017 ◽

2017 ◽

Vol IV-1/W1 ◽

pp. 215-222 ◽

Cited By ~ 2

Author(s):

A. Maas ◽

F. Rottensteiner ◽

C. Heipke

Keyword(s):

Classical Method ◽

Image Data ◽

Training Data ◽

Label Noise ◽

Training Technique ◽

Class Labels ◽

Noise Tolerant ◽

Changes Over Time ◽

Over Time

Supervised classification of remotely sensed images is a classical method for change detection. The task requires training data in the form of image data with known class labels, whose manually generation is time-consuming. If the labels are acquired from the outdated map, the classifier must cope with errors in the training data. These errors, referred to as label noise, typically occur in clusters in object space, because they are caused by land cover changes over time. In this paper we adapt a label noise tolerant training technique for classification, so that the fact that changes affect larger clusters of pixels is considered. We also integrate the existing map into an iterative classification procedure to act as a prior in regions which are likely to contain changes. Our experiments are based on three test areas, using real images with simulated existing databases. Our results show that this method helps to distinguish between real changes over time and false detections caused by misclassification and thus improves the accuracy of the classification results.

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Classification of Fashion Article Images Based on Improved Random Forest and VGG-IE Algorithm

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001420510040 ◽

2019 ◽

Vol 34 (03) ◽

pp. 2051004

Author(s):

Jian Liu ◽

Yuchen Zheng ◽

Ke Dong ◽

Haitong Yu ◽

Jianjun Zhou ◽

...

Keyword(s):

Genetic Algorithm ◽

Random Forest ◽

Image Recognition ◽

Classification Accuracy ◽

Recommendation System ◽

Computation Time ◽

Training Data ◽

Computational Time ◽

Time Problem

In classification of fashion article images based on e-commerce image recommendation system, the classification accuracy and computation time cannot meet the actual requirements. Herein, for the first time to our knowledge, we present two diverse image recognition approaches for classification of fashion article images called random-forest method based on genetic algorithm (GA-RF) and Visual Geometry Group-Image Enhancement algorithm (VGG-IE) to solve classification accuracy and computation time problem. In GA-RF, the number of segmentation times and the decision trees are the key factors affecting the classification results. Improved genetic algorithm is introduced into the parameter optimization of forests to determine the optimal combination of the two parameters with minimal manual intervention. Finally, we propose six different Deep Neural Network architectures, including VGG-IE, to improve classification accuracy. The VGG-IE algorithm uses batch normalization and seven kinds training-data augmentation for ease and promotion of learning process. We investigate the effectiveness of the proposed method using Fashion-MNIST dataset and 70[Formula: see text]000 pictures, Experimental results demonstrate that, in comparison with the state-of-the-art algorithms for 10 categories of image recognition, our VGG algorithm has the shortest computational time when it satisfies certain classification accuracy. VGG-IE approach has the highest classification accuracy.

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REAL-TIME BLOB-WISE SUGAR BEETS VS WEEDS CLASSIFICATION FOR MONITORING FIELDS USING CONVOLUTIONAL NEURAL NETWORKS

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-iv-2-w3-41-2017 ◽

2017 ◽

Vol IV-2/W3 ◽

pp. 41-48 ◽

Cited By ~ 34

Author(s):

A. Milioto ◽

P. Lottes ◽

C. Stachniss

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Sugar Beet ◽

Real Time ◽

Convolutional Neural Networks ◽

Image Data ◽

High Quality ◽

Sugar Beets ◽

Quality Classification

UAVs are becoming an important tool for field monitoring and precision farming. A prerequisite for observing and analyzing fields is the ability to identify crops and weeds from image data. In this paper, we address the problem of detecting the sugar beet plants and weeds in the field based solely on image data. We propose a system that combines vegetation detection and deep learning to obtain a high-quality classification of the vegetation in the field into value crops and weeds. We implemented and thoroughly evaluated our system on image data collected from different sugar beet fields and illustrate that our approach allows for accurately identifying the weeds on the field.

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Prospectivity Mapping of Mineral Deposits in Northern Norway Using Radial Basis Function Neural Networks

Minerals ◽

10.3390/min9020131 ◽

2019 ◽

Vol 9 (2) ◽

pp. 131 ◽

Cited By ~ 3

Author(s):

Cyril Juliani ◽

Steinar Ellefmo

Keyword(s):

Radial Basis Function ◽

Basis Function ◽

Input Data ◽

Mean Squared Error ◽

Training Data ◽

Northern Norway ◽

Radial Basis ◽

Host Rocks ◽

Selection Of

In this paper, the radial basis function neural network (RBFNN) is used to generate a prospectivity map for undiscovered copper-rich (Cu) deposits in the Finnmark region, northern Norway. To generate the input data for RBFNN, geological and geophysical data, including up to 86 known mineral occurrences hosted in mafic host-rocks, were combined at different resolutions. Mineral occurrences were integrated into “deposit” and “non-deposit” training sets. Running RBFNN on different input vectors, with a k-fold cross-validation method, showed that increasing the number of iterations and radial basis functions resulted in: (1) a reduction of training mean squared error (MSE) down to 0.1, depending on the grid resolution, and (2) reaching correct classification rates of 0.9 and 0.6 for training and validation, respectively. The latter depends on: (1) the selection of “non-deposit” training data throughout the study area, (2) the scale at which data was acquired, and (3) the dissimilarity of input vectors. The “deposit” input data were correctly identified by the trained model (up to 83%) after proceeding to classification of non-training data. Up to 885 km2 of the Finnmark region studied is favorable for Cu mineralization based on the resulting mineral prospectivity map. The prospectivity map can be used as a reconnaissance guide for future detailed ground surveys.

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The Comparison of Density-Based Clustering Approach among Different Machine Learning Models on Paddy Rice Image Classification of Multispectral and Hyperspectral Image Data

Agriculture ◽

10.3390/agriculture10100465 ◽

2020 ◽

Vol 10 (10) ◽

pp. 465

Author(s):

Shiuan Wan ◽

Yi-Ping Wang

Keyword(s):

Feature Selection ◽

Hyperspectral Image ◽

Image Data ◽

Classification Performance ◽

Hyperspectral Images ◽

Computational Time ◽

Multispectral Images ◽

Linear Discriminant ◽

Hyperspectral Image Data

The analysis, measurement, and computation of remote sensing images often require enhanced unsupervised/supervised classification approaches. The goal of this study is to have a better understanding of (a) the classification performance of multispectral image and hyperspectral image data; (b) the classification performance of unsupervised and supervised models; and (c) the classification performance of feature selection among different models. More specifically, the multispectral images and hyperspectral images with high spatial resolution are well accepted for improving land use and classification. Hence, this study used multispectral images (WorldView-2) and hyperspectral images (CASI-1500) and focused on the classifiers K-means, density-based spatial clustering of applications with noise (DBSCAN), linear discriminant analysis (LDA), and back-propagation neural network (BPN). Then the feature selection (principle component analysis, PCA) on four classifiers is studied. The results show that the image material of CASI-1500 classification accuracy is slightly better than that of WorldView-2. The overall classification of BPN is the best, the overall data has a κ value of 0.89 and the overall accuracy is 97%. The DBSCAN presents a reality with good accuracy and the integrity of the thematic map. The DBSCAN can attain an accuracy of about 88% and save 95.1% of computational time.

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UAV-based training for fully fuzzy classification of Sentinel-2 fluvial scenes

10.5194/egusphere-egu2020-10659 ◽

2020 ◽

Author(s):

Patrice Carbonneau ◽

Barbara Belletti ◽

Marco Micotti ◽

Andrea Casteletti ◽

Stefano Mariani ◽

...

Keyword(s):

Neural Networks ◽

Spatial Resolution ◽

Low Cost ◽

Super Resolution ◽

Training Data ◽

Fuzzy Classification ◽

Training Phase ◽

Small Channels ◽

Sentinel 2

In current fluvial remote sensing approaches, there exists a certain dichotomy between the analysis of small channels at local scales which is generally done with airborne data and the analysis of entire basins at regional and national scales with satellite data. One possible solution to this challenge is to use low-altitude imagery from low-cost UAVs to provide sub-metric scale class information which can then be used to train fuzzy classification models for entire Sentinel 2 tiles. The fuzzy classification approach can allow for sub-pixel information and when extended to entire Sentinel 2 tiles, the method therefore develops information at a resolution of less than 10 meters (the best spatial resolution of Sentinel 2 bands) at regional scales. In this contribution, we present such a method where UAV imagery is used as the training data for the fully fuzzy classification of Sentinel 2 imagery. We partition the fluvial corridor in three simple classes: water, dry sediment and vegetation.&#160; Then we manually classify the local UAV imagery into highly accurate class rasters. In order to augment the value of the Sentinel 2 data, we use an established super-resolution method that delivers 10 meter spatial resolution across all 11 Sentinel 2 bands. We then use the sub-metric UAV classifications as training data for the 10 meter super-resolved Sentinel 2 imagery and we train fuzzy classification models using random forests, dense neural networks and convolutional neural networks (CNN). We find that CNN architectures perform best and can predict class membership within a pixel of a new Sentinel 2 tile not seen in the training phase with a mean error of 0% and an RMS error of 18%. Crisp class predictions derived from the fuzzy models range in accuracy from 88% to 99%, even in the case of tiles never seen in the training phase. With this approach, it is now possible to deploy a low-cost UAV in order to train a transferable CNN model that can predict fuzzy classes at very large scales from freely available Sentinel 2 imagery. This approach can therefore serve as the basis for multi temporal classification and change detection of the Sentinel 2 archives.

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