Image Recognition




Viewing an object comes naturally to us and doesn’t require much effort from us to differentiate between objects, recognize a face or read a sign as our brain is good at understanding images. When it comes to the computer, these tasks are highly difficult to accomplish.

What is image recognition?

Image recognition is the ability of the technologies to identify objects, places, actions, people, and writing in images. It is a part of Computer Vision that identifies and detects an object or an attribute in an image or a video.
Image recognition has become an integral part of many cutting-edge technologies and has found use cases such as facial recognition in our smartphones, the autonomous mode in driverless cars, diagnostic imaging in the healthcare sector and others.

How does Image Recognition Work?

Image Recognition process comprises the following major steps:

  • Collect & Organize Data

A computer perceives an image either as a vector image or as a raster image. Vector images are a set of polygons that are color annotated while raster images comprise of a sequence of pixels with numerical values for colors.
In order to analyze images, constructs representing the physical features, formed via geometrical encoding are logically assessed by the computer. Then the data is organized through classification and feature extraction.
Image classification involves simplifying the image by extracting the important information alone thus converting an image into a feature vector.

  •  Build a Predictive Model

A trained classification algorithm processes the feature vector as points in high dimensional space and gives class label as output. The next step is to detect planes or surfaces that separate the higher dimensional space in such a way that the examples belonging to a particular class are on the same side of the surface or the plane. Neural networks that stimulate our brain are used to build predictive models. A neural network is a group of interconnected nodes which depend on learning algorithms to estimate functions that require a huge amount of unknown inputs. In the case of image classification, numerous image recognition algorithms are available such as support vector machines, bag-of-words, K-nearest neighbors, face landmark estimation, logistic regression, and others.

  • Use the model to recognize images

In this step, two types of image data – training and test data are organized in a proper way so as to remove any duplicates between the two. The resulting data is then fed into the image recognition model for recognizing images. The model relies on large databases and emerging patterns to make sense of images.


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