Object recognition is a subfield of computer vision, artificial intelligence, and machine learning that seeks to recognize and identify the most prominent objects (i.e., people or things) in a digital image or video with AI models. Image recognition is also a subfield of AI and computer vision that seeks to recognize the high level contents of an image.
How Is Object Recognition Different from Image Recognition?
If you’re familiar with the domain of computer vision, you might think that object recognition sounds very similar to a related task: image recognition. However, there’s a subtle yet important difference between image recognition and object recognition:
- In image recognition, the AI model assigns a single high-level label to an image or video.
- In object recognition, the AI model identifies each and every noteworthy object in the image or video.
The best way to illustrate the difference between object recognition and image recognition is through an example. Given a photograph of a soccer game, an image recognition model would return a single label such as “soccer game.” An object recognition model, on the other hand, would return many different labels corresponding to the different objects (e.g., the players, the soccer ball, the goal, etc.), as well as their positions in the image.
Object recognition is also not quite the same as another computer vision task called object detection:
- Object recognition models are given an image or video, with the task of identifying all the relevant objects in it.
- Object detection models are given an image or video as well as an object class, with the task of identifying all the occurrences of that object (and only that object).
For example, suppose you have an image of a street scene:
- An object detection model would take this image as input as well as an object class such as “pedestrian” or “car,” and then return all the detected locations in the image where that object occurs.
- An object recognition model, on the other hand, would return the locations of both pedestrians and cars, as well as all other objects it recognizes in the image (buildings, street signs, etc.).
You can therefore think of object detection as a “filter” on the output of general object recognition models, looking only for a specific type of object.
How Are Object Recognition Models Trained?
To perform object recognition, machine learning experts train AI models on extremely large datasets of labeled data. Each member of the dataset includes the source image or video, together with a list of the objects it contains and their positions (in terms of their pixel coordinates).
By “studying” this dataset and learning from its mistakes, the AI model gradually improves its capability to recognize different classes of objects during AI training, just as humans learn to recognize different visual concepts.
Once the model has been trained on a preexisting dataset, it can start analyzing fresh real-world input. For each image or video frame, the model creates a list of predictions for the objects it contains and their locations. Each prediction is assigned a confidence level—i.e., how much the model believes the prediction represents a real-world object. Predictions that are above a given threshold are classified as objects, and they become the final output of the system.
How Are Image Recognition Models Trained?
The AI model training process for image recognition is similar to that of object recognition. However, there’s one crucial difference: the labels for the input dataset.
Object recognition datasets bundle together an image or video with a list of objects it contains and their locations. Image recognition datasets, however, bundle together an image or video with its high-level description.
Before training an image recognition model, machine learning experts need to decide which categories they would like the AI model to recognize. For example, a simple weather recognition model might classify images as “sunny,” “cloudy,” “rainy,” or “snowy.” Each image or video in the training dataset needs to be associated with one of these labels, so that the model can learn it during the training process.
Once the image recognition model is trained, it can start analyzing real-world data. The model accepts an image as input, and returns a list of predictions for the image’s label. As with object recognition, each prediction has a confidence level. The prediction with the highest confidence level is selected as the system’s final output.
What Is Object Recognition Used for?
Object recognition has many practical use cases. Below are just a few applications of object recognition:
- In retail AI, object recognition models can identify different products and brands on the shelves to analyze how customers interact with and purchase them.
- In geospatial AI, wildlife researchers can use object recognition on drone footage to analyze how animal populations change in an area over time.
- In media AI, sales and marketing professionals can use object recognition to identify “objects” such as logos, brands, and products to better understand the contents of an image.
- Autonomous vehicles require object recognition to identify the most relevant parts of the world around them (e.g., pedestrians, road signs, or other cars).
Facial authentication can also be considered a special case of object recognition in which a person’s face is the “object” that must be detected. Modern facial recognition systems can detect thousands of different faces with extremely high accuracy in just a fraction of a second.
What is Image Recognition Used For?
Like object recognition, image recognition is used in a wide variety of industries and applications. Below are some examples:
- In manufacturing AI, image recognition models can examine products and classify them as “defective” or “non-defective.”
- In security AI, construction sites can use image recognition to make sure that workers are wearing their personal protective equipment (PPE), classifying surveillance images as “compliant” or “non-compliant.” (Click here to see a video of a PPE detection model in action.)
- In healthcare AI, physicians can use image recognition models to analyze the output of medical imaging devices. For example, an AI trained on mammogram images can classify the machine’s output as “benign” or “potentially cancerous,” flagging it for review by a human expert.
Why Use Chooch for Object Recognition?
Chooch is a powerful, feature-rich computer vision platform for building object recognition and image recognition models. We’ve helped businesses of all sizes, industries, and technical levels deploy and manage visual AI and computer vision solutions.
Thanks to Chooch, there’s no need to hire your own in-house team of AI and machine learning experts. Instead, you can hit the ground running with one of our dozens of pre-trained object recognition models that have been designed to fit a wide range of business use cases. You can also leverage the Chooch AI platform to train your own highly accurate object recognition model using a custom dataset, and then deploy it in the cloud or with an edge AI platform.
Why Use Chooch for Image Recognition?
The Chooch AI platform makes it simple to get started creating your own robust, production-ready image recognition and object recognition models. From within the Chooch dashboard, you can select one of our 100+ pre-trained AI models, or create a custom model based on a specific dataset. Our user-friendly AI platform lets you easily label and annotate dataset images and dramatically shorten the training process.
Ready to start building sophisticated, highly accurate image recognition and object recognition AI models? So are we. If you’re comfortable delving into the technical details, feel free to check out our computer vision API. Otherwise, you can schedule a call with our team of AI experts for a chat about your business needs and objectives, or create your free account on the Chooch computer vision platform.
Why Use Chooch for Object Recognition?
Chooch is a powerful, feature-rich computer vision platform for building object recognition and image recognition models. We’ve helped businesses of all sizes, industries, and technical levels deploy and manage visual AI and computer vision solutions.
Thanks to Chooch, there’s no need to hire your own in-house team of AI and machine learning experts. Instead, you can hit the ground running with one of our dozens of pre-trained object recognition models that have been designed to fit a wide range of business use cases. You can also leverage the Chooch AI platform to train your own highly accurate object recognition model using a custom dataset, and then deploy it in the cloud or with edge AI platform.
Ready to start building sophisticated, highly accurate object recognition AI models? So are we. If you’re comfortable delving into the technical details, feel free to check out our computer vision API. Otherwise, you can schedule a call with our team of AI experts for a chat about your business needs and objectives, or create your free account on the Chooch computer vision platform.
