What is the AI Image Recognition in Computer Vision?

Published on Thursday February 25, 2021 4 min read By Anolytics
What is the AI Image Recognition in Computer Vision?

Artificial Intelligence (AI) is becoming intellectual as it is exposed to machines for recognition. The massive number of databases stored for Machine Learning models, the more comprehensive and agile is your AI to identify, understand and predict in varied situations.

To perceive the world of surroundings image recognition helps the computer vision to identify things accurately. Without image recognition, it is impossible to detect or recognize objects. As image recognition is essential for computer vision, hence we need to understand this more deeply.

What is AI Image Recognition?

Being a part of computer vision, image recognition is the art of detecting and analyzing images with the motive to identify the objects, places, people, or things visible in one’s natural environment. Ultimately, the main motive remains to perceive the objects as a human brain would. Image recognition aims to detect and analyzes all these things and draws a conclusion from such analysis.

Though, computer vision is a wider term that comprises the methods of gathering, analyzing, and processing the data from the real world to machines. Image recognition analyses each pixel of an image to extract useful information similarly to humans do. AI cameras can detect and recognize various objects developed through computer vision training.

Also Read: How to Improve AI Security Camera Performance with Image Annotation Services

How Does AI Image Recognition Work?

Humans recognize images using the natural neural network that helps them to identify the objects in the images learned from their past experiences. Similarly, the artificial neural network works to help machines to recognize the images.

In AI neural network there are multiple layers of neurons can affect each other. And the complexities of structure and architecture of neural network depends on the types of information required. Image recognition is more complicated than you think as there are various things involved like deep learning, neural networks, and sophisticated image recognition algorithms to make this possible for machines.

How AI is Trained to Recognize the Image?

To make image recognition possible through machines, we need to train the algorithms that can learn and predict with accurate results. Let’s take an example – if you look at the image of a cat, you can easily tell it is a cat, but the image recognition algorithm works differently.

Also Read: How To Improve Machine Learning Model Performance: Five Ways

Due to similar attributes, a machine can see it 75% cat, 10% dog, and 5% like other similar looks like an animal that are referred to as confidence score. And to predict the object accurately, the machine has to understand what exactly sees, then analyze comparing with the previous training to make the final prediction.

Neural Networks in AI Image Recognition

Machines visualize and analyze the visual content in images differently from humans. Compare to humans, machines perceive images as a raster which a combination of pixels or through the vector. Convolutional neural networks help to achieve this task for machines that can explicitly explain what going on in images.

A convolutional neural network is right now assisting AI to recognize the images. But the question arises how varied images are made recognizable to AI. The answer is, these images are annotated with the right data labeling techniques to produce high-quality training datasets.

How Does AI Recognize Images?

While recognizing the images, various aspects considered helping AI to recognize the object of interest. Let’s find out how and what type of things are identified in image recognition.

Facial Recognition

Recognizing the face by AI is one of the best examples in which a face recognition system maps various attributes of the face. And after gathering such information process the same to discover a match from the database.

Also Read: How Semantic Segmentation & Landmark Annotation Improves Facial Recognition

Smartphone makers are nowadays using the face recognition system to provide security to phone users. They can unlock their phone or various applications into their device. Though, your privacy may compromise, as your data might be collected without your concern.

But with the time being such problems will solved with more improved datasets generated through landmark annotation for face recognition.

Object Recognition

Image recognition is performed to recognize the object of interest in that image. Visual search technology works by recognizing the objects in the image and look for the same on the web.

Text Detection

Text detection is useful for OCR transcription, where the text is extracted from the image and make available for the other users like text classification or text annotation to create datasets for NLP-based machine learning model development.

Pattern recognition

Similarly to recognize a certain pattern in a picture image recognition is used. Like face expressions, textures, or body actions performed in various situations.

Image Annotation for Object Recognition by AI Model

Making object recognition becomes possible with data labeling service. Human annotators spent time and effort in manually annotating each image producing a huge quantity of datasets. Machine learning algorithms need the bulk of the huge amount of training data to make train the model.

Also Read: Top Data Labeling Challenges Faced by the Data Annotation Companies

Though, in unsupervised machine learning, there is no such requirement, while in supervised machine learning without labeled datasets it is not possible to develop the AI model. And if you want your image recognition algorithm to become capable of predicting accurately, you need to label your data.

In data annotation, thousands of images are annotated using various image annotation techniques assigning a specific class to each image. Usually, most AI companies don’t spend their workforce or deploy such resources to generate the labeled training datasets.

Also Read: Why Data Annotation is Important for Machine Learning and AI

Outsourcing is a great way to get such jobs done by dedicated experts at a lower cost. Companies involved in data annotation do this job better helping AI companies save their cost of training an in-house labeling team and money spend on other resources.

Anolytics is the industry leader in providing high-quality training datasets for machine learning and deep learning. Working with renowned clients, it is offering data annotation for computer vision and NLP-based AI model developments.

Also Read: Five Reasons Why You Need To Outsource Your Data Annotation Project

World-class infrastructure, certified with international data security standards, Anolytics offers a great platform to get datasets for diverse sectors. Working with a fully scalable solution, it works with a collaborative approach making AI possible in diverse unknown fields.

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