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How to Classify Image Application

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Image classification refers to a process in computer vision that can classify an image according to its visual content. For example, an image classification algorithm may be designed to tell if an image contains a human figure or not. An algorithm such as SURF is used to generate features.
Image classification refers to the task of extracting information classes from a multiband raster image. The resulting raster from image classification can be used to create thematic maps. The recommended way to perform classification and multivariate analysis is through the Image Classification toolbar.
The objective of image classification is to identify and portray, as a unique gray level (or color), the features occurring in an image in terms of the object or type of land cover these features actually represent on the ground. Image classification is perhaps the most important part of digital image analysis.
Image classification is a supervised learning problem: define a set of target classes (objects to identify in images), and train a model to recognize them using labeled example photos. Early computer vision models relied on raw pixel data as the input to the model.
Image Classification helps us to classify what is contained in an image. Image Localization will specify the location of single object in an image whereas Object Detection specifies the location of multiple objects in the image. Finally, Image Segmentation will create a pixel wise mask of each object in the images.
Object-based Classification. Object-based classification is a two-step process, first the image is segmented or broken into discrete objects or features with and then each object is classified. This type of classification attempts to mimic the type of analysis done by humans during visual interpretation.
Object recognition is a computer vision technique for identifying objects in images or videos. Object recognition is a key output of deep learning and machine learning algorithms. The goal is to teach a computer to do what comes naturally to humans: to gain a level of understanding of what an image contains.
Object detection involves detecting instances of objects from a particular class in an image. The goal of object detection is to detect all instances of objects from a known class, such as people, cars or faces in an image.
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