颜色分类数据集

颜色分类是一个重要的应用程序，在许多领域都有应用。例如，每天执行。具有最优超平面寿命分析的支持向量机分类器可以从该分类过程中受益。对于分类过程，可以采用多种分类算法。其中，最流行的机器学习算法有神经网络、决策树、 k 最近邻、贝叶斯网络、支持向量机等。在这项训练工作中，使用支持向量机，并尝试获得一个分类器模型。支持向量机算法是其中一种监督式学习方法。和所有的监督式学习方法一样，SVM 要求解决回归和分类问题。该算法通常用于对不同标记样本进行分离和分类的训练。利用支持向量机进行训练，目的是创建一个最优超平面，并对数据进行不同类别的分类。这个超平面位于尽可能远离数据，以避免误差条件。数据集包含了大约80个用于整个颜色类的训练集数据集的图像和90个用于测试集的图像。为此应用准备的颜色是 y 黄色、黑色、白色、绿色、红色、橙色、蓝色和紫色。在这个实现中，基本颜色是分类的首选颜色。并创建了一个包含这些基本颜色图像的数据集。数据集还包括所有图像的掩码。我们通过二值化图像来创建这些掩码。我们对我收集的图像进行屏蔽，并将属于类颜色的像素绘制为白色，剩余的像素绘制为黑色。

Color classification is an important application with widespread use across numerous fields. For instance, it is executed on a daily basis. Support vector machine (SVM) classifiers with optimal hyperplane lifetime analysis can benefit from this classification process. A variety of classification algorithms can be adopted for this classification workflow. Among them, the most popular machine learning algorithms include Neural Networks, Decision Trees, k-Nearest Neighbors (k-NN), Bayesian Networks, Support Vector Machines, and others. In this training task, Support Vector Machines are utilized to develop a classifier model. The SVM algorithm is a type of supervised learning method. Like all supervised learning approaches, SVM is designed to solve both regression and classification problems. This algorithm is typically used for training to separate and classify differently labeled samples. The goal of training with SVM is to create an optimal hyperplane to classify data into different categories, which is positioned as far away from the data as possible to avoid error conditions. The dataset contains approximately 80 images for the training set across all color classes and 90 images for the test set. The colors prepared for this application are y-yellow, black, white, green, red, orange, blue, and purple. In this implementation, basic colors are the preferred categories for classification. A dataset containing images of these basic colors has been constructed. The dataset also includes masks for all images. We generate these masks through image binarization. We mask the collected images by painting the pixels belonging to the target color classes as white, and the remaining pixels as black.

搜集汇总

背景与挑战

背景概述

颜色分类数据集包含80个训练图像和90个测试图像，涵盖8种基本颜色，并附带图像掩码，适用于机器学习和深度学习中的颜色分类任务。