What is Image Classification

The classification includes a broad range of decision-theoretic approaches to identifying images (or parts thereof). All classification algorithms are based on the assumption that the image in question depicts one or more features (e.g., geometric details in the case of a manufacturing classification system, or spectral regions in the point of remote sensing, as shown in the examples below) and that each of these features belongs to one of several distinct and exclusive classes. The classes may be specified a priori by an analyst (as in supervised classification) or automatically clustered (i.e. as in unsupervised classification) into sets of prototype classes, where the analyst merely specifies the number of desired categories. (Classification and segmentation have closely related objectives, as the former is another form of component labeling that can result in the segmentation of various features in a scene.)


Image classification is the process of assigning land cover classes to pixels. It 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. Depending on the interaction between the analyst and the computer during classification, there are two types of classification: supervised and unsupervised. Image classification plays an important role in environmental and socioeconomic applications. In order to improve classification accuracy, scientists have laid paths in developing advanced classification techniques.

Figure 1 Image classification

Image classification is perhaps the most important part of digital image analysis. It is very nice to have a “pretty picture” or an image, showing a magnitude of colors illustrating various features of the underlying terrain, but it is quite useless unless to know what the colors mean.

How does It work?

Image classification analyzes the numerical properties of various image features and organizes data into categories. Classification algorithms typically employ two phases of processing: training and testing. In the initial training phase, characteristic properties of typical image features are isolated, and, based on these, a unique description of each classification category, i.e. training class, is created. In the subsequent testing phase, these feature-space partitions are used to classify image features. The description of training classes is an extremely important component of the classification process. In supervised classification, statistical processes (i.e. based on an a priori knowledge of probability distribution functions) or distribution-free processes can be used to extract class descriptors. The unsupervised classification relies on clustering algorithms to automatically segment the training data into prototype classes. In either case, the motivating criteria for constructing training classes are that they are:


  • Independente.a change in the description of one training class should not change the value of another,
  • Discriminatorye.different image features should have significantly different descriptions, and
  • Reliable, all image features within a training group should share the common definitive descriptions of that group.

A convenient way of building a parametric description of this sort is via a feature vector, where n is the number of attributes, which describe each image feature and training class. This representation allows us to consider each image feature as occupying a point, and each training class as occupying a sub-space (i.e. a representative point surrounded by some spread, or deviation), within the n-dimensional classification space. Viewed as such, the classification problem is that of determining to which sub-space class each feature vector belongs.

Image Classification Technique

Two major categories of image classification techniques include unsupervised (calculated by the software) and supervised (human-guided) classification:

  1. Unsupervised Classification

Unsupervised classification is where the outcomes (groupings of pixels with common characteristics) are based on the software analysis of an image without the user providing sample classes. The computer uses techniques to determine which pixels are related and groups them into classes. The user can specify which algorism the software will use and the desired number of output classes but otherwise does not aid in the classification process. However, the user must have knowledge of the area being classified when the groupings of pixels with common characteristics produced by the computer have to be related to actual features on the ground (such as wetlands, developed areas, coniferous forests, etc.)

  1. Supervised Classification

Supervised classification is based on the idea that a user can select sample pixels in an image that are representative of specific classes and then direct the image processing software to use these training sites as references for the classification of all other pixels in the image. Training sites (also known as testing sets or input classes) are selected based on the knowledge of the user. The user also sets the bounds for how similar other pixels must be to group them together. These bounds are often set based on the spectral characteristics of the training area, plus or minus a certain increment (often based on “brightness” or strength of reflection in specific spectral bands). The user also designates the number of classes that the image is classified into. Many analysts use a combination of supervised and unsupervised classification processes to develop final output analysis and classified maps.


[1] “Image Classification”, Tutorial, available online at:  http://www.microimages.com/documentation/Tutorials/classify.pdf

[2] What is image classification? Available online at: http://desktop.arcgis.com/en/arcmap/latest/extensions/spatial-analyst/image-classification/what-is-image-classification-.htm

[3] S. V. S. Prasad , T. Satya Savithri and Iyyanki V. Murali Krishna, “Techniques in Image Classification; A Survey”, Global Journal of Researches in Engineering: F Electrical and Electronics Engineering, Volume 15, Issue 6, Version 1.0, Year 2015.

What is Machine Vision and How It Works

Vision plays a fundamental role for living beings by allowing them to interact with the environment in an effective and efficient way. Where human vision is best for qualitative interpretation of a complex, unstructured scene, machine vision excels at quantitative measurement of a structured scene because of its speed, accuracy, and repeatability. For example, on a production line, a system can inspect hundreds, or even thousands, of parts per minute. A machine vision system built around the right camera resolution and optics can easily inspect object details too small to be seen by the human eye.

What is it?

Machine vision (also called “industrial vision” or “vision systems”) is the use of digital sensors (wrapped in cameras with specialized optics) that are connected to processing hardware and software algorithms to visually inspect pretty much anything. It is a truly multi-disciplinary field, encompassing computer science, optics, mechanical engineering, and industrial automation. While historically the tools were focused on manufacturing, that’s quickly changing, spreading into medical applications, research, and even movie making.

This is the technology to replace or complements manual inspections and measurements with digital cameras and image processing. The technology is used in a variety of different industries to automate production, increase production speed and yield, and improve product quality.

Machine vision in operation can be described by a four-step flow:

  • Imaging: Take an image.
  • Processing and analysis: Analyze the image to obtain a result.
  • Communication: Send the result to the system in control of the process.
  • Action: Take action depending on the vision system’s result.

Figure 1: Machine Vision Operations

Machine-Vision System Components

It allows you to obtain useful information about physical objects by automating the analysis of digital images of those objects. This is one of the most challenging applications of computer technology. There are two general reasons for this: almost all vision tasks require at least some judgment on the part of the machine, and the amount of time allotted for completing the task usually is severely limited. While computers are astonishingly good at elaborate, high-speed calculation, they still are very primitive when it comes to judgment.

Such systems have five key components.

  • Illumination: Just as a professional photographer uses lighting to control the appearance of subjects, the use must consider the color, direction, and shape of an illumination. For objects moving at high speed, a strobe often can be used to freeze the action.
  • Camera: For many years, the standard machine-vision camera has been monochromatic. It outputs many shades of gray but not color, provides about 640 × 480 pixels, produces 30 frames per second, uses CCD solid-state sensor technology, and generates an analog video signal defined by television standards.
  • Frame Grabber: A frame grabber interfaces the camera to the computer that is used to analyze the images. One common form for a frame grabber is a plug-in card for a PC.
  • Computer: Often an ordinary PC is used, but sometimes a device designed specifically for image analysis is preferred. The computer uses the frame grabber to capture images and specialized software to analyze them and is responsible for communicating results to automation equipment and interfacing with human operators for setup, monitoring, and control.
  • Software: The key to successful performance is the software that runs on the computer and analyzes the images. Software is the only component that cannot be considered a commodity and often is a vendor’s most important intellectual property.

Machine Vision Goals

The goals can be divided into the following from a technical point of view:

Strategic Goal  Applications
Higher quality Inspection, measurement, gauging, and assembly verification
Increased productivity Repetitive tasks formerly done manually are now done by the System
Production flexibility Measurement and gauging / Robot guidance / Prior operation verification
Less machine downtime and reduced setup time Changeovers programmed in advance
More complete information and tighter process control Manual tasks can now provide computer data feedback
Lower capital equipment costs Adding vision to a machine improves its performance, avoids obsolescence
Lower production costs One vision system vs. many people / Detection of flaws early in the process
Scrap rate reduction Inspection, measurement, and gauging
Inventory control Optical Character Recognition and Identification
Reduced floor space Vision system vs. operator


[1] “Machine Vision Introduction”, Version 2.2, December 2006, SICK IVP, available online at: www.sickivp.com

[2] Bill Silver, Cognex, “An Introduction to Machine Vision a Tutorial”, available online at: https://www.evaluationengineering.com/an-introduction-to-machine-vision-a-tutorial

[3] “Introduction to Machine Vision: A guide to automating process & quality improvements”, available online at: www.cognex.com

[4] David Phillips, “Machine vision: a survey”, Western CEDAR, 2008.

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