Image recognition, in the context of machine vision, is the ability of software to identify objects, places, people, writing, actions in images. Computers can use machine vision technologies in combination with a camera and AI software to achieve image recognition. The global image recognition market size is projected to reach USD 86.32 billion by 2027, exhibiting a CAGR of 17.6% during the forecast period.
Image semantic segmentation links each pixel in an image to a class label called semantic segmentation. Semantic segmentation is the classification at the pixel level. Sematic segmentation can identify named entities, annotate, and classify them to facilitate their retrieval.
While some software allows automatic or manual segmentation and content labeling, the process leaves you with raw data. Perfect memory goes further as it combines this functionality with artificial intelligence to analyze and understand information and make it truly useful for businesses.
A semantic segmentation annotation tool uses instance identification to detect all the entities (places, people, or objects) present in video content or image content, then label and categorize the pixels.
Using the power of semantics and artificial intelligence, the AI provides automated instance detection and tagging to identify and classify objects with pixel-level accuracy.
Perfect Memory’s semantic segmentation tool then interprets these datasets using rule-based machine learning algorithms to provide the user with the most relevant and precise information about their content.
The main features within a segmentation annotation tool are:
- Classification: allows AI to recognize objects present in a frame and categorize them in classes (“cats, “dogs”).
- Object detection: finds out where these objects are in the picture, obtains the exact coordinates, and annotates the objects.
- Image segmentation: detects every object belonging to the assigned classes with pixel precision.
- Instance segmentation: attributes an ID to each object of the same class (“Cats 1”, “Cats 2”)
After the segmentation part, which consists of dividing the content into homogeneous sections, the annotation feature, which allows extraction and recognition of any visual instance before automatically labeling it.
How businesses can gain greater intelligence through semantic segmentation
Semantic segmentation is a powerful tool that can support businesses in optimizing how they distribute and monetize content. The technology uses deep learning algorithms to bring the exact object of interest from an image or video and can be used further for processing like recognition and description at the pixel level.
The application can vary in the number of areas, provide an excellent understanding of the texture of a surface and a lens to the whole area of studies. There are important components in image-based searches with numerous applications:
- Surface defects on steel sheets
- Satellite imagery
- Landscape recognition
- Recognizing building
- Architecture prints
- Printed Circuit Board, components on the boards, Solder
- Medical imaging like cancer nucleus detection or lung-disease
- Facial detection
- Surgery planning
- Cracks, holes, and leaks
- Retail and fashion industries
- Traffic control systems
- Self-driving cars
- Check people in the area have the correct PPE equipment on
- Farming, crop detection
- Roads, potholes, lane detection
- Weld’s detection
A semantic segmentation annotation tool can help companies improve their content’s accessibility for every audience. By segmenting precise sections within content, the AI allows businesses to easily access the exact pixel-level video or image segments necessary based on relevant data.
How to label images for semantic segmentation
While there are many unreliable and inefficient labeling tools, choosing the right one is important. To annotate images in semantic segmentation, outline the object carefully by using points or using a pen tool. Make sure to touch each end to cover the object entirely that will be shaped with a specific color to differentiate the object from the nearby objects. After drawing, use the list of objects or instances and create objects having separate regions. Using this technique objects can be configured with nested classifications. Ground truth masks are made which allow users to train an image segmentation model. This process is completed and can be a daunting task.
Top image segmentation architectures
These are only a handful of architectures available today.
- Mask R-CNN
Image semantic segmentation is a challenge. One of the main issues between all the architecture is to consider the global visual context of the input to improve the prediction of the segmentation. The pixel-level prediction over the entire image allows a better comprehension of the environment with high precision. Image understanding is also approached with keypoint detection, action recognition, video captioning.
Tim Goebel is the Data & Artificial Intelligence Principal Consultant at SafeNet Consulting. Tim is an IT engineering leader with a track record of success bringing projects from conception through completion to enhance efficacy, control costs, and propel business growth. In early 2021 Tim achieved his Microsoft Certified: Azure AI Fundamentals certificate that verifies he has demonstrated foundational knowledge of machine learning (ML) and artificial intelligence (AI) concepts and related Microsoft Azure services.
Tim also has certifications and critical technical knowledge on Visual Studio C#, Scikit learn, Keras, Tensorflow, Python, NLTK, Tokenizing, Panda, Docker, MathWorks, labview, Open CV, RNN, CNN, and more to meet business needs.