Image classification based on improved random forest algorithm

Man, Weishi; Ji, Yuanyuan; Zhang, Zhiyu

doi:10.1109/icccbda.2018.8386540

Cited by 12 publications

(10 citation statements)

References 10 publications

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“…While forming a decision tree, it is calculated with the information gain and information gain rate approaches according to which criterion or attribute value of the branch in the tree (Ozcan et al, 2020). DT is a variant of a greedy algorithm that progresses in the form of dividing and conquering in a top-down repetition, applying a set of decision rules (Man et al, 2018). In this algorithm, a tree structure is created, and class tags are expressed in the leaves of the tree.…”

Section: Decision Treesmentioning

confidence: 99%

Automatic Ship Detection and Classification using Machine Learning from Remote Sensing Images on Apache Spark

Dolapci¹,

Ozcan²

2021

Journal of Intelligent Systems: Theory and Applications

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Ship detection and classification is very important for port and coastal security. Due to maritime safety and traffic control, highresolution images of ships should be obtained. High resolution color remote sensing ship images taken from short distances provide advantages in ship detection applications. But the analysis of these high-dimensional images is complicated and requires long time. Dividing the image data into smaller blocks and representing them with a vector with distinctive and independent features facilitates the analysis process. For this reason, a block division method is applied first, dividing the image data into small pixel blocks. These obtained image blocks are also represented by the hybrid feature vectors. These feature vectors are created by adding the sub-features extracted from the color and texture properties of the images one after another. Using the obtained hybrid vectors, the images are classified using machine learning methods on Apache Spark. Classification studies were realized using Naive Bayes, Decision Trees and Random Forest methods in the MLlib. The analysis of the images was realized much faster with the clustering architecture created on Apache Spark platform. According to the obtained classification results, 99.62% classification success was achieved by using Random Forest method. In addition, an average of 3.4 times acceleration was achieved by running each method on 1 master + 4 workers clustering architecture on Spark.

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Section: Decision Treesmentioning

confidence: 99%

Automatic Ship Detection and Classification using Machine Learning from Remote Sensing Images on Apache Spark

Dolapci¹,

Ozcan²

2021

Journal of Intelligent Systems: Theory and Applications

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“…The nodes are branched, and tree structures are formed according to the determined splitting rules such as maximum information gain, maximum information gain rate, and minimum Gini index by using the created training data. The information gain and the Gini index obtained by using attributes a to divide the sample set D is showed with given node splitting formula below [31].…”

Section: Random Forest In Spark Mllibmentioning

confidence: 99%

Fast texture classification of denoised SAR image patches using GLCM on Spark

Ozcan¹,

Ersoy²,

Ogul³

2020

Turk J Elec Eng & Comp Sci

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Classification of a synthetic aperture radar (SAR) image is an essential process for SAR image analysis and interpretation. Recent advances in imaging technologies have allowed data sizes to grow, and a large number of applications in many areas have been generated. However, analysis of high-resolution SAR images, such as classification, is a time-consuming process and high-speed algorithms are needed. In this study, classification of high-speed denoised SAR image patches by using Apache Spark clustering framework is presented. Spark is preferred due to its powerful open-source cluster-computing framework with fast, easy-to-use, and in-memory analytics. Classification of SAR images is realized on patch level by using the supervised learning algorithms embedded in the Spark machine learning library. The feature vectors used as the classifier input are obtained using gray-level cooccurrence matrix which is chosen to quantitatively evaluate textural parameters and representations. SAR image patches used to construct the feature vectors are first applied to the noise reduction algorithm to obtain a more accurate classification accuracy. Experimental studies were carried out using naive Bayes, decision tree, and random forest algorithms to provide comparative results, and significant accuracies were achieved. The results were also compared with a state-of-the-art deep learning method. TerraSAR-X images of high-resolution real-world SAR images were used as data.

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“…A high smoothness value indicates that the image has a smooth intensity. Smoothness can be calculated by the formula (6).…”

Section: Histogram Feature Extractionmentioning

confidence: 99%

“…Along with the development of information technology, it is possible to identify fruit maturity with the computers help [3] [4]. Identification can be done by classifying the image of tomatoes with various methods such as K-Nearest Neighbor (KNN) [4] [5], Random Forest [6], Support Vector Machine(SVM) [7] [8], Naïve Bayes [7] [8] [9] [10] [11] [12] [13], etc. In this study, identification of maturity in a set of tomatoes using the Naïve Bayes algorithm.…”

Section: Introductionmentioning

confidence: 99%

Tomato Maturity Classification using Naive Bayes Algorithm and Histogram Feature Extraction

Kusuma

Setiadi

Putra

2018

JAIS

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Tomatoes have nutritional content that is very beneficial for human health and is one source of vitamins and minerals. Tomato classification plays an important role in many ways related to the distribution and sales of tomatoes. Classification can be done on images by extracting features and then classifying them with certain methods. This research proposes a classification technique using feature histogram extraction and Naïve Bayes Classifier. Histogram feature extractions are widely used and play a role in the classification results. Naïve Bayes is proposed because it has high accuracy and high computational speed when applied to a large number of databases, is robust to isolated noise points, and only requires small training data to estimate the parameters needed for classification. The proposed classification is divided into three classes, namely raw, mature and rotten. Based on the results of the experiment using 75 training data and 25 testing data obtained 76% accuracy

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Image classification based on improved random forest algorithm

Cited by 12 publications

References 10 publications

Automatic Ship Detection and Classification using Machine Learning from Remote Sensing Images on Apache Spark

Automatic Ship Detection and Classification using Machine Learning from Remote Sensing Images on Apache Spark

Fast texture classification of denoised SAR image patches using GLCM on Spark

Tomato Maturity Classification using Naive Bayes Algorithm and Histogram Feature Extraction

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