This project uses a traditional machine learning approach to answer this problem. Traditional ML classifiers are easy to understand and implement, fast and provide good explanations of the data and predictions. However, their performance is compromised when the data is …

Dec 14, 2023 · Deep Learning algorithms, such as CNN are the most used method to assign a class and a label to an image. CNN can automatically learn and extract features from the images, such as edges,...

Sep 28, 2024 · This article explains how to adapt Graph Neural Networks (GNNs) for image classification. It covers the process from converting images into graphs to updating the model’s parameters.

Dec 5, 2024 · In this blog, we will discuss how to perform image classification using machine learning using four popular algorithms: Random Forest Classifier, KNN, Decision Tree Classifier, and Naive Bayes classifier. We will then jump into implementation step-by-step.

In this paper, we use topological features automatically extracted from graph represen-tations of images for image classification. Our approach is simple, intuitive, and generic. We compare our method against a traditional feature descriptor, histogram of oriented gradients (HOG) on the MNIST data set.

Mar 19, 2025 · Machine learning (ML) plays a crucial role in automating image classification, eliminating the need for manual labeling. Traditional image classification methods relied on handcrafted features, but modern ML techniques use deep learning models to extract patterns and features automatically.

May 11, 2021 · In this report, we implement an image classifier using both classic computer vision and deep learning techniques. Specifically, we study the performance of a Bag of Visual Words classifier using Support Vector Machines, a Multilayer Perceptron, an existing architecture named InceptionV3 and our own CNN, TinyNet, designed from scratch.

Taking SVM and CNN as examples, this paper compares and analyzes the traditional machine learning and deep learning image classification algorithms.

Aug 5, 2019 · In this paper, we use topological features automatically extracted from graph representations of images for image classification. Our approach is simple, intuitive, and generic. We...

We propose CNN2GNN and CNN2Transformer which instead leverage inter-example information for classification. We use Graph Neural Networks (GNNs) to build a latent space bipartite graph and compute cross-attention scores between input images and a proxy set. Our approach addresses several challenges of existing methods.