What are the two types of image classification method?
Image classification is the process of assigning a label to a given image, such as “cat”, “dog”, or “tiger”. There are two main types of image classification methods:
Supervised Classification
The supervised classification technique is based on the idea that a user can select specific pixels from an image to become a focal point for classifying the image. The samples selected are representative of a given group and are used as references in order to classify the whole group. Training sets are used to figure out what to teach. You’ll use a bounding box that defines how alike other pixels are before it’s grouped as the same thing. For instance, if you’re looking at a picture of fruit, you might group red apples together and green grapes together.
The user also assigns the number of classes they want to classify the image as. To achieve a statistical characterization for each information class, the image is classified by examining the reflectance for each pixel and comparing it to the signatures with which it most closely resembles. Supervised classification uses classification algorithms and regression techniques to develop predictive models. The algorithms include linear regression, logistic regression, neural networks, decision tree, support vector machine, random forest, naive Bayes, and k-nearest neighbor.
Unsupervised Classification
Unsupervised classification is when the outcome (groupings of pixels with similar features) is determined with the help of software analysis on an image without user input. The computer will determine what type of pixel it is. You can choose which algorithm and the number of output classes you want, but the user doesn’t have any say on how to classify words/phrases within a sentence. However, the user should have knowledge of the field being classified as when the groupings of pixels with common characteristics produced by a computer programmer have to be related to actual features on the ground.
Clustering algorithms categorize data without the use of a predetermined grouping, while neural networks use a series of connected nodes to process data in similar ways to the human brain. The more input, output and weight layers there are, the more detailed and richer the analysis becomes.