Implementing Image Classification Using Svm Stack Overflow

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Find centralized, trusted content and collaborate around the technologies you use most. Bring the best of human thought and AI automation together at your work. Support Vector Machines (SVMs) are a type of supervised machine learning algorithm that can be used for classification and regression tasks. In this article, we will focus on using SVMs for image classification. When a computer processes an image, it perceives it as a two-dimensional array of pixels. The size of the array corresponds to the resolution of the image, for example, if the image is 200 pixels wide and 200 pixels tall, the array will have the dimensions 200 x 200...

The first two dimensions represent the width and height of the image, respectively, while the third dimension represents the RGB color channels. The values in the array can range from 0 to 255, which indicates the intensity of the pixel at each point. In order to classify an image using an SVM, we first need to extract features from the image. These features can be the color values of the pixels, edge detection, or even the textures present in the image. Once the features are extracted, we can use them as input for the SVM algorithm. The SVM algorithm works by finding the hyperplane that separates the different classes in the feature space.

The key idea behind SVMs is to find the hyperplane that maximizes the margin, which is the distance between the closest points of the different classes. The points that are closest to the hyperplane are called support vectors. One of the main advantages of using SVMs for image classification is that they can effectively handle high-dimensional data, such as images. Additionally, SVMs are less prone to overfitting than other algorithms such as neural networks. This tutorial provides a comprehensive guide on image classification using Support Vector Machines (SVM) with Python's scikit-learn library. It also delves into K-Nearest Neighbors (KNN) and Decision Trees, allowing you to compare these machine learning techniques for image classification.

Create a Virtual Environment (Recommended): Using Anaconda Prompt or your terminal, create and activate a virtual environment named image_classification: Run the Jupyter Notebook: -After installation of libraries , double-click to run the code. The code provided in the Image_Classification.ipynb notebook covers the following steps: In a previous tutorial, we explored using the Support Vector Machine algorithm as one of the most popular supervised machine learning techniques implemented in the OpenCV library. So far, we have seen how to apply Support Vector Machines to a custom dataset that we have generated, consisting of two-dimensional points gathered into two classes.

In this tutorial, you will learn how to apply OpenCV’s Support Vector Machine algorithm to solve image classification and detection problems. After completing this tutorial, you will know: Kick-start your project with my book Machine Learning in OpenCV. It provides self-study tutorials with working code. Communities for your favorite technologies. Explore all Collectives

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Image Classification with Support Vector Machine (SVM) We have install Anaconda Distribution 2022.10 which ships with Python 3.9. It provides meta packages for Python versions 3.7, 3.8, and 3.10 like numpy, matploitlib, math.dist() and pillow which we used in our program. Anaconda Distribution can also support up to three versions of Python at a time. Opencv can be install directly from the anaconda terminal using pip comand. To install Anaconda on Linux; Step 1: Update system repositories using the update command Step 2: Install curl package as it permits fetching the installation script of Anaconda Step 3: Prepare Anaconda Installer

The executions were done on spyder from anaconda. 1- upload the image.csv and edgeHistogram.csv files to the same directory of the execution file or copy the link to the image. 2- in the load data section of the code, replace the various csv file names with your CSV file names or link to your files. 3- We then run the code on spyder to perform the SVM classification on the data; 4- First the code loads the data from the files, then convert to numpy array. The arrays are then split into test and train sets then classified and finally evaluated to test how well the classification was done by printing the accuracy, classification report and a graph of results. There was an error while loading.

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