In the current Artificial Intelligence and Machine Learning industry, “Image Recognition”, and “Computer Vision” are two of the hottest trends. Both these fields involve working with identifying visual characteristics, which is the reason most of the time, these terms are often used interchangeably. Despite some similarities, both computer vision and image recognition represent different technologies, concepts, and applications.
In this article, we will be comparing Computer Vision & Image Recognition by dwelling into their differences, similarities, and methodologies used. So let’s get started.
What is Image Recognition?
Image Recognition is a branch in modern artificial intelligence that allows computers to identify or recognize patterns or objects in digital images. Image Recognition gives computers the ability to identify objects, people, places, and texts in any image.
The main aim of using Image Recognition is to classify images on the basis of pre-defined labels & categories after analyzing & interpreting the visual content to learn meaningful information. For example, when implemented correctly, the image recognition algorithm can identify & label the dog in the image.
How Image Recognition Works?
Fundamentally, an image recognition algorithm generally uses machine learning & deep learning models to identify objects by analyzing every individual pixel in an image. The image recognition algorithm is fed as many labeled images as possible in an attempt to train the model to recognize the objects in the images.
The image recognition process generally comprises the following three steps.
- Gathering and Labeling Data
The first step is to gather and label a dataset with images. For example, an image with a car in it must be labeled as a “car”. Generally, larger the dataset, better the results.
- Training the Neural Networks on the Dataset
Once the images have been labeled, they will be fed to the neural networks for training on the images. Developers generally prefer to use Convolutional Neural Networks or CNN for image recognition because CNN models are capable of detecting features without any additional human input.
- Testing & Prediction
After the model trains on the dataset, it is fed a “Test” dataset that contains unseen images to verify the results. The model will use its learnings from the test dataset to predict objects or patterns present in the image, and try to recognize the object.
What is Computer Vision?
Computer Vision is a branch in modern artificial intelligence that allows computers to identify or recognize patterns or objects in digital media including images & videos. Computer Vision models can not only analyze an image to recognize or classify an object within an image, but also react to those objects.
The main aim of a computer vision model goes further than just detecting an object within an image, but it’s to interact & react to those objects. For example, in the image below, the computer vision model can not only identify the object in the frame, a scooter, but it can also track the movement of the object within the frame.
How Computer Vision Works?
A computer vision algorithm works just as an image recognition algorithm does, by using machine learning & deep learning algorithms to detect objects in an image by analyzing every individual pixel in an image. The working of a computer vision algorithm can be summed up in the following steps.
- Data Acquisition and Preprocessing
The first step is to gather a sufficient amount of data that can include images, GIFs, videos, or live streams. The data is then preprocessed to remove any noise or unwanted objects.
- Feature Extraction
The training data is then fed to the computer vision model to extract relevant features from the data. The model then detects and localizes the objects within the data, and classifies them as per predefined labels or categories.
- Semantic Segmentation & Analysis
The image is then segmented into different parts by adding semantic labels to each individual pixel. The data is then analyzed and processed as per the requirements of the task.
Image Recognition v/s Computer Vision : How Do They Differ?
Although both image recognition and computer vision function on the same basic principle of identifying objects, they differ in terms of their scope & objectives, level of data analysis, and techniques involved. Let’s discuss each of them individually.
- Scope and Objectives
The main objective of image recognition is to identify & categorize objects or patterns within an image. The primary goal is to detect or recognize an object within an image. On the other hand, computer vision aims at analyzing, identifying or recognizing patterns or objects in digital media including images & videos. The primary goal is to not only detect an object within the frame, but also react to them.
- Level of Analysis
The most significant difference between image recognition & data analysis is the level of analysis. In image recognition, the model is concerned only with detecting the object or patterns within the image. On the flip side, a computer vision model not only aims at detecting the object, but it also tries to understand the content of the image, and identify the spatial arrangement.
For example, in the above image, an image recognition model might only analyze the image to detect a ball, a bat, and a child in the frame. Whereas, a computer vision model might analyze the frame to determine whether the ball hits the bat, or whether it hits the child, or it misses them all together.
- Complexity
Image recognition algorithms generally tend to be simpler than their computer vision counterparts. It’s because image recognition is generally deployed to identify simple objects within an image, and thus they rely on techniques like deep learning, and convolutional neural networks (CNNs)for feature extraction.
Computer vision models are generally more complex because they detect objects and react to them not only in images, but videos & live streams as well. A computer vision model is generally a combination of techniques like image recognition, deep learning, pattern recognition, semantic segmentation, and more.
Image Recognition Vs. Computer Vision: Are They Similar?
Despite their differences, both image recognition & computer vision share some similarities as well, and it would be safe to say that image recognition is a subset of computer vision. It’s essential to understand that both these fields are heavily reliant on machine learning techniques, and they use existing models trained on labeled dataset to identify & detect objects within the image or video.
Final Thoughts
To sum things up, image recognition is used for the specific task of identifying & detecting objects within an image. Computer vision takes image recognition a step further, and interprets visual data within the frame.
