• Corpus ID: 212513832

A REVIEW OF IMAGE CLASSIFICATION TECHNIQUES

@inproceedings{Thakur2017ARO,
  title={A REVIEW OF IMAGE CLASSIFICATION TECHNIQUES},
  author={Nupur Thakur and Deepa B. Maheshwari},
  year={2017}
}
1,2Electronics and Telecommunication, Pune Institute of Computer Technology, Pune-Satara Road, Behind Bharati Vidyapeeth College, Dhankawadi, Pune, Maharashtra, India. ------------------------------------------------------------------------***------------------------------------------------------------------------Abstract Image classification is an important tool for extracting information from digital images. The aim of this paper is to summarize information about few image classification… 

Rider and Sunflower optimization-driven neural network for image classification

A novel classifier named RideSFO-NN is developed for image classification based on Neural Network (NN) classifier, which is trained using an optimization approach, which achieves the maximal accuracy, maximal sensitivity, and maximal specificity based on K-Fold.

3D Texture Feature Extraction and Classification Using GLCM and LBP-Based Descriptors

The proposed technique improves the discrimination power and achieves promising results even if the number of images per class is relatively small, outperforms other handcrafted 3D or 2D texture feature extraction methods and typical deep-learning networks.

Early Detection of Diseases in Precision Agriculture Processes Supported by Technology

This research proposes the use of convolutional neural networks to detect diseases in horticultural crops and concludes that for disease detection in tomato crops, the custom model has better execution time and size, and the classification performance is acceptable.

Optimized Negative Selection Algorithm for Image Classification in Multimodal Biometric System

It was discovered in this work that global feature selection improves recognition accuracy in biometric systems.

Comparison of Satellite Images Classification Techniques using Landsat-8 Data for Land Cover Extraction

  • Soha Ahmed
  • Environmental Science, Mathematics
    International Journal of Intelligent Computing and Information Sciences
  • 2021
Comparing the effectiveness of four classification algorithms including ISODATA, K-means, pixel-based and segment-based classification techniques to attain accurate land cover extraction from remote sensing data revealed that the pixel- based classification presented a superior in terms of the overall accuracy and kappa coefficient.

A Review on Land Cover Classification Techniques for Major Fruit Crops in India - Present Scenario and Future Aspects

Comparison suitability of unsupervised classifiers (ISODATA and K-Means), supervised classifier (Parellepiped, Mahalanobis Distance, Maximum Likelihood and ANN) and Decision Tree Classification (DTC) for crop classification are reviewed.

Fuzzy machine learning approach for transitioned building footprints extraction using dual-sensor temporal data

Using proposed fuzzy approach, transitioned building footprints were accurately identified compared to traditional techniques and used as input in Modified Possibilistic c-means classifier for transitionedBuilding footprints extraction.

A survey on satellite image classification approaches

The main objective of this paper is to explore classification based on training sample, and considers two approaches: supervised image classification and unsupervised classification.

Image Processing Techniques for Analysis of Satellite Images for Historical Maps Classification—An Overview

An exhaustive analysis on the merits and demerits of many satellite image processing methods are discussed in this paper to support the selection of innovative solutions for the different problems associated with satellite imageprocessing applications.

Application of remote sensing and geographical information system in mapping land cover of the national park

The study was conducted with the objective of mapping landscape cover of Nechsar National park in Ethiopia to produce spatially accurate and timely information on land use and changing pattern.

References

SHOWING 1-10 OF 20 REFERENCES

Comparative study of distinctive image classification techniques

To conclude it has been shown that the proposed system Hybrid RGSA and Support Vector Machine Framework is the paramount one to classify images competently.

A Survey on Image Classification Methods

It has shown that Self Organizing Tree Algorithm, an unsupervised classification method classify the images to 81.5% even it contain blurry and noisy content, proved that it is the best classification method.

A comparison of neural network and fuzzy c-means methods in bladder cancer cell classification

  • Y. HuK. Ashenayi R. Bonner
  • Computer Science
    Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94)
  • 1994
It is found, based on the data from 467 cell images from six cases, the neural network approach achieves a classification rate of 96.9% while fuzzy c-means scores 76.5%.

A Review of Soft Classification Approaches on Satellite Image and Accuracy Assessment

Two supervised soft classifiers, FCM and PCM have been used to demonstrate the improvement in the classification accuracy by membership vector, RMSE, and also it has tried to generate fraction output from FCM, PCM, and noise with entropy.

Unsupervised PolSAR Image Classification Using Discriminative Clustering

This paper designs an energy function for unsupervised PolSAR image classification by combining a supervised softmax regression model with a Markov random field smoothness constraint and iteratively optimize the classifiers and class labels by alternately minimizing the energy function with respect to them.

A COMPARATIVE STUDY OF SUPERVISED IMAGE CLASSIFICATION ALGORITHMS FOR SATELLITE IMAGES

The techniques considered in this paper are Minimum Distance, k-Nearest Neighbour (KNN), Nearest Clustering Fuzzy C-Means (FCM) and Maximum Likelihood (ML) Classification algorithms.

Comparing different classifications of satellite imagery in forest mapping ( Case study : Zagros forests in Iran )

Forest mapping is essential to manage natural resources and environment, land use plans and also to determine land potential and it is defined as one of the main resource for adjusting development

Combination of hard and soft classification method based on adaptive threshold

In the agricultural landscapes of Southeast Beijing City, results from the proposed model were assessed at a range of spatial scales and accuracy assessment showed that hard and soft classification model could get better results.

Satellite Image Classification Methods and Techniques: A Review

The current research work is a study on satellite image classification methods and techniques and compares various researcher’s comparative results on satellite images classification methods.