Image classification is a basic procedure in computer vision and it includes categorizing pictures into built-in classes depends on their content. Machine Learning is the most trending topics of current time we aim in offering trending research and dissertation ideas that matches to your needs. Unique and Novel research ideas will be shared we work in a different framework that you attain success in your academics. By using the Deep Learning (DL), Convolutional Neural Networks (CNNs) we design the go-to model for image classification due to their extraordinary performance.

The following is the processing steps on how we produce an image classification project using Machine Learning (ML).

  1. Objective Definition

     We define the main aim of our work is to design a ML framework the classifies images into predefined categories.

  1. Data Collection
  • Dataset: Based on our application we need to gather our own dataset. There are some famous datasets such as CIFAR-10, CIFAR-100, ImageNet, and MNIST which is better to begin our model.
  • Ensure we labeled data, defining every image has a related category.
  1. Data Preprocessing
  • Image Cropping: We make sure that entire images have the identical dimensions which are necessary in most ML models.
  • Normalization: The pixel values should in the range [0, 1] when they are starting in [0,255]. For this we normalize and standardize the data.
  • Augmentation of Data: By preparing more training data by deploying scattered conversions such as rotations, flips, shifts, and zooms which we make use of it to generalize our model.
  1. Model choosing and Construction
  • Convolutional Neural Networks (CNNs): These are the most usual DL models utilized for image classification contains the famous structures such as VGG, ResNet, and MobileNet that help us.
  • Transfer Learning: We implement a pre-trained model which is trained on a large dataset like ImageNet and fine-tune it for our particular task rather than instructing it from initial stage. This leads us in fast and better outcomes.
  • Existing ML models such as SVM, k-NN are useful to us when the dataset is small, but their robustness is basically inferior to CNNs for large-scale image classification.
  1. Training the Framework
  • Split the Data: We divide our dataset into training, validation and test sets.
  • Instructing: When using CNNs we analyze GPU acceleration to train our framework by training dataset.
  • Validation: Testing and fine-tune our model using test set for avoiding overfitting.
  1. Model Validation
  • To evaluate the model’s efficiency we utilize the test dataset.
  • General metrics like accuracy, precision, recall, F1-score, and confusion matrix are tested in our framework.
  1. Optimization & Hyperparameter Tuning (optional)
  • We adapt hyperparameters such as learning rate, batch size, number of layers, and number of filters for our model.
  • Analyzing methods such as dropout and batch normalization for enhancing CNN performance and balancing.
  1. Deployment
  • Once we get satisfied of our model’s efficiency, we apply it as part in mobile app, web service and environments where image categorization is required.
  1. User Interface (if suitable)
  • Developing an interface where our users can upload and capture pictures and receive classification outcomes in real-world scenarios.
  • The review systems assist us in refining our framework by gathering user-tested samples.
  1. Conclusion & Future Enhancements
  • We outline our project’s results, challenges faced and the possible applications.
  • Understand the additional developments like,
  • Incorporating real-time classifications abilities.
  • We combine the classifier into huge systems like an e-commerce product categorizer.
  • Extending the number of categories in our model.

Guidelines:

  • Large-scale Training: We analyze instructing on cloud platforms and specialized hardware for huge datasets and difficult frameworks.
  • Class Imbalance: When few classes have certainly some examples we consider approaches like oversampling, under sampling and using stable periodic samplers.

       Image classification has a vast number of applications from medical imaging to autonomous cars. By using the appropriate method and techniques, we design an effective image classification system that fulfills our particular requirements.

Image Classification using Machine Learning Ideas

Image Classification using Machine Learning Thesis Topics

Get to know the relevant thesis ideas and the thesis topics that we have worked .Contact us for more details we also carry out tailored thesis topics assistance.

  1. Machine Learning Algorithms for Satellite Image Classification Using Google Earth Engine and Landsat Satellite Data: Morocco Case Study

Keywords:

Remote sensing, satellite image, supervised classification, Google earth engine, LANDSAT satellite, machine learning algorithms.

            Our paper uses Landsat 8 satellite data to perform a land cover classification of morocco by utilizing ML methods. We proposed six supervised ML techniques such as SVM, CART, RF, MD, DT, GTB o built-up areas, water areas etc… to deduce at the end best performing classifier have high accuracy. To enhance the outcome we additionally used NDVI, NDBI, BSI and MNDWI. We compare these metrics to get the high accuracy. 

  1. GLCM Based Feature Extraction and Medical X-ray Image Classification Using Machine Learning Techniques

Keywords:

GLCM, Machine Learning, random forest, SMOTE

            We proposed medical x-ray images that are classified and we can extract the features by utilizing an ensemble learning method. We can extract the image features by utilizing the GLCM feature extraction method. Our proposed method can able to find the difference among sick and healthy images. To enhance the efficiency of ensemble learning classification method we can compare them with different methods by utilizing the performance indicators LR, GNB as well as RF.

  1. Machine Learning and Deep Learning Models for Vegetable Leaf Image Classification Based on Data Augmentation

Keywords:

Classification, Vegetable plant leaf, Image preprocessing, RGB image, Data augmentation, Deep learning, Computer vision

            Our paper collects types of RGB images after that we have to utilize data augmentation techniques, we found that the blending of rotation, flipping, shift, and zoom approaches to enhance the performance of DL model. These can go along the augmented data of DL and ML to process and train different model. The DL method (resnext50) has the high accuracy.

  1. Hybrid Approach for Classification of Medical Imaging Data Using Machine Learning Techniques

Keywords:

Supervised machine learning, Hybrid approach, Support vector machine, Medical imaging chest X-ray pneumonia dataset

            Our proposed model implements a high approach on the medical imaging data through ML methods. We used the supervised ML methods like DT, Ensemble classification, RF and KNN. Our proposed method comprises of two states: first state classifies the data into two classes namely pneumonia image or normal image with various ML methods. Our proposed hybrid approach gives the best accuracy.

  1. Comparative Analysis of Machine Learning Algorithms in Vehicle Image Classification

Keywords:

Vehicle detection, Image filtering, Data mining

            Our paper uses weka to classify vehicle images into three vehicle groups based on data mining methods. In feature extraction we used three image filtering methods that are Colour Layout, Edge Histogram, and Pyramid Histogram of Oriented Gradients (PHOG). We can extract the feature to fed into five classification methods like MLP, Simple Logistic, SMO, LMT, RF. Our PHOG filter method gives the best accuracy.

  1. A Comparison of Machine Learning and Deep Learning in Hyperspectral Image Classification

Keywords:

Hyperspectral image classification, Convolutional neural network (CNN), k-nearest neighbors (k-NN), Artificial Neural Networks (ANN), Fully Convolutional Network (FCN)

            We use different ML and DL methods to perform classification on hyperspectral images are investigated and related. We used three popular ML methods like SVM, KNN, ANN are utilized for hyperspectral image classification then another two deep structures in CNN. Three datasets were used and we have to perform excellent in 3D CNN DL methods and this method is more beneficial when compare to ML methods. 

  1. Maize Leaf Healthy and Unhealthy Classification Using Image Processing Technique and Machine Learning Classifiers

Keywords:

Feature extraction, PNN

            Our paper proposes an effective ML method to classify the healthy or unhealthy classification according to the leaf image present. We also evaluate the color feature extraction by utilizing RGB mean and standard deviation and classification by utilizing PNN and KNN techniques. We also used five image processing methods like image preprocessing, image segmentation, feature extraction, classification and performance analysis. 

  1. Performance Comparison of Various Machine Learning Algorithms for Ultrasonic Fetal Image Classification Problem

Keywords:

Principal component analysis (PCA), Decision tree classifier (DT), Classifier, Multi-layer perceptron (MLP)

            We offer a novel approach performance by the comparison of ML classifiers for classification of ultrasonic fetal images. We also offer Gabor feature extraction and different classification methods to classify three various class of ultrasound image. At first Gabor features are gained from raw images and to remove redundancy in feature and dimensionality PCA can be used. At last the features were gained from PCA were fed to different ML methods and the performance can be estimated. 

  1. Machine Learning-Based Multi-temporal Image Classification Using Object-Based Image Analysis and Supervised Classification

Keywords:

Object-based approach, Land cover classification, Change detection, Remote sensing

We offer a hybrid method of object-based image analysis and the supervised classification can be utilized. We used the data that is high-resolution multispectral 4-band images offered by PlanetScope satellite of region. Initially we preprocess the data and that can pass through pipeline followed by multi-resolution segmentation method and we can classify the image into seven class based on spectral signature using algorithms like maximum likelihood (ML), SVM, MD.  

  1. Fashion Images Classification using Machine Learning, Deep Learning and Transfer Learning Models

Keywords:

deep learning (DL), image classification, transfer learning, Fashion classification

            The aim of our paper is to use ML and DL methods to classify and find the image. We used the ML methods like SVM, KNN, DT, RF and the DL methods like CNN, AlexNet, GoogleNet, LeNet, LeNet5 and the transfer learning methods like VGG16, MobileNet and ResNet50. We train and test our model by online google colaboratory that support ML and DL methods. 

Important Research Topics