In today’s digital era, images have become an integral part of our lives. From product photography to graphic design and machine learning applications, seamlessly eliminating backgrounds has become a game-changer in visual communication. Beyond its practical applications, background removal serves as a transformative tool, emphasizing the subject, enhancing aesthetics, and elevating brand appeal. In this blog post, we delve into the methods of how background can be removed from images as well as its applications, empowering photographers, designers, and marketers alike to unlock new levels of creativity, storytelling, and visual impact.
What is background removal?
Background removal is a technique that extracts the main subject from an image while eliminating the unwanted elements in the background. Whether you’re an e-commerce retailer looking to display products on a clean backdrop or a photographer seeking to isolate subjects for creative compositions, background removal has a wide range of applications.
How does background removal work?
Background removal in images is achieved through various techniques, ranging from manual methods to sophisticated algorithms used in software applications. Some of the best-known methods are described below.
Manual selection
The simplest method involves manually selecting the foreground objects using tools like lasso, pen, or magnetic selection. This process requires time and precision, as the user carefully outlines the object. Once selected, the background can be deleted or replaced with a transparent layer.
Color-based segmentation
In cases where the foreground and background have distinct color differences, segmentation techniques can be used. By identifying color clusters, algorithms can separate the foreground from the background.
Chroma key composition
This technique is widely used in video and film production. A subject is filmed or photographed in front of a colored screen (usually, green or blue), which is then replaced with a different background using specialized software. The solid color is easy to distinguish and remove, leaving the subject intact.
Deep learning techniques
With the advent of deep learning, convolutional neural networks (CNNs) have been employed for background removal tasks. The most common CNN-based background removal technique is called semantic segmentation. Semantic segmentation models can identify and label different objects within an image, including the background. These networks are trained on large datasets of images with known foreground-background pairs, enabling them to learn to distinguish between the two. By removing the background class, the desired foreground object can be isolated.
The quality of the remaining subject cut out of the background highly depends on the complexity of the image as well as the removal technique chosen. The best quality results can be achieved by utilizing the newest advancements in technology, mainly by using deep learning and semantic segmentation methods. With large datasets and extensive training, these models can accurately distinguish between foreground and background, even in complex images with intricate details.
The least accurate results are achieved by using manual selection, since it is time-consuming when done carefully, as well as color-based segmentation, which works well when the foreground and background have distinct colors, yet might struggle with similar color pallets.
No matter which background removal method is chosen, it is important to remember that the accuracy can be subjective and depend on individual use cases. For professional applications or critical tasks, combining multiple methods may yield the best results.
Image Segmentation Explored
There are two types of image segmentation – semantic segmentation and instance segmentation.
Semantic segmentation
This approach to image segmentation detects the area of the image that contacts an object of a certain label / type. Semantic segmentation is useful where the user needs to detect patterns and abstract objects.
Instance segmentation
Instance segmentation is more advanced and has a different set of use cases. This approach detects individual objects of a certain label / type.
Training an Instance Segmentation Model
Training an instance segmentation model requires a dataset of labeled images. These labeled images will require either polygon and / or bitmap labeled objects, both of which can be created using the SentiSight.ai annotation tools. For detailed instructions on using the tools to create bitmap and polygon labels, please refer to the user guides.
The SentiSight.ai web interface enables you to train an instance segmentation model without the need for coding or computer vision expertise.
Once you have your labeled images, simply head over to the model training interface. From there, you can set the model name, training time and the stop time.
The stop time will determine how long the model will continue training if there is no improvement measured in terms of mean Average Precision (mAP).
For more advanced users, there are additional parameters that can be selected and customized.
Once you have trained an instance segmentation model, you could then use this as a basis for a background removal tool powered by AI.
Applications
Applications of background removal are diverse and essential in various industries. This tool enhances the visual appeal of marketing materials, product photography, and social media content by isolating subjects. It also fuels creativity for artists and photographers, enabling unique compositions and collages. Key applications for the background removal tool are described below.
Enhancing Visual Appeal
One of the key advantages of background removal is its ability to improve the visual appeal of an image. By isolating the subject, you can create stunning visuals that draw the viewer’s attention to the intended focal point. Whether you’re creating marketing material, product catalogs, or social media content, background removal ensures that your images are visually captivating and professional.
E-commerce and Product Photography
For e-commerce businesses, displaying products against a clutter-free background is crucial for boosting sales. Background removal allows you to replace the original background with a solid color, a transparent layer, or a contextually appropriate backdrop. This not only provides a consistent and visually appealing presentation but also facilitates comparison between different products and creates a sense of professionalism.
Creative Compositions
Photographers and graphic designers leverage background removal to unlock their creativity. By removing the background, they can effortlessly blend subjects into new environments, experiment with unique collages, or seamlessly integrate them into other visual elements. Background removal offers immense flexibility and opens up a world of possibilities for artistic expression and storytelling.
Training Data Preparation
In the realm of machine learning and computer vision, accurate and reliable training data is essential. Background removal plays a vital role in creating high-quality datasets for various tasks, such as object detection, segmentation, and recognition. By removing backgrounds, researchers and developers can isolate subjects and ensure that their models focus solely on the relevant features, improving their performance and efficiency.
SentiSight have recently launched a background removal pre-trained model powered by Image Recognition AI, helping you to efficiently and accurately remove the background of images at scale.
Introducing the SentiSight background removal tool
The Sentisight platform provides an intuitive background removal tool as a pre-trained model, offering users the convenience of immediate use without the need to train their own model. This feature allows users to effortlessly remove backgrounds from images in a hassle-free manner.
Why use our tool?
There are two main benefits of using the SentiSight background removal tool; scalability and accuracy.
Whilst some other tools may only allow you to remove the background from one image at a time, our platform enables you to process images in batches, helping to speed up the process. Available to use via API (as well as on the web interface and mobile app), the SentiSight pre-trained model can be deployed to automatically remove the background from a large volume of images at once. Our advanced image recognition AI ensures that the background removal results are accurate and precise as well.
How to use the background removal tool
There are three ways you can use the background removal tool for your project, these being:
- Web Interface – Utilizing SentiSight.ai’s pre-trained models via the Web Interface offers the fastest and most straightforward approach, especially ideal when scalability is not a concern.
- REST API – By employing the REST API for SentiSight.ai’s pre-trained models, the users gain immense flexibility and scalability without the expense of dedicated hardware like GPUs.
- Mobile App – The SentiSight.ai mobile app allows users to effortlessly perform image classification predictions on their phones and upload images to the projects.
Web Interface
Using the background removal tool on the SentiSight online platform web interface is the quickest and most straightforward way to remove the background from images using our tools. This is a popular approach if you are trying out the models, or your project uses do not require scalability.
Using the background removal tool via the web interface can be achieved in such simple steps:
- Log in to your SentiSight.ai account
- Select Pre-trained models > Background removal
- Click on the Upload images and import the images you desire to remove the background from
- Review the results and download the resulting images
REST API
Using the background removal tool via REST API offers you a significant degree of flexibility and scalability to remove the background from images without the need for expensive hardware such as GPUs or your own custom solutions.
Mobile App
Using the SentiSight mobile app gives you the ability to use the background removal pre-trained model from your phone, as well as being able to add images to your project datasets.
Get Started
As a pre-trained model, you can get started with the SentiSight background removal tool right away without the need for model training or coding expertise. The easiest and quickest way to get started is to use the web interface platform, where you can make a free account here. Once you have made your account, to use the background removal tool:
- Click on the ‘pre-trained models’ dropdown menu
- Choose the background removal tool
- Perform the background removal on your images!
Conclusion
Background removal is a transformative technique that empowers users to enhance visual appeal, boost e-commerce sales, enable creative compositions, and prepare high-quality training data for machine learning applications.
SentiSight.ai stands at the forefront of this technological advancement, offering powerful background removal tool capabilities within its user-friendly platform.
