With modern AI-based systems, machines are capable of distinguish between two images. This capability is evoked in a software using multiple techniques, one such technique is semantic segmentation for labeling data. Want to learn more about it, read ahead...
Computer vision applications are gaining importance due to their ability to help machines comprehend images the same way humans do. One of the significant capabilities of these computer vision applications is "Image Processing," which enables machines to use digital images and deep learning models to identify and classify objects and react to them accurately. Preparing images for computer vision models that perform image recognition and object detection is critical.
Popular machine learning classification approaches, such as deep learning, require high-quality labeled data volumes. Data labeling, called data annotation, is part of the pre-processing stage when developing a machine learning (ML) model. It requires identifying raw data (i.e., images, text files, videos) and then adding one or more labels to that data to specify its context for the models, allowing the machine learning model to make accurate predictions.
Businesses use software, different techniques, and data annotators to clean, arrange, and label data. This training data is used to build machine learning models. These labels help analysts to segregate variables within datasets, allowing them to pick the best data predictors for ML models. The labels determine which data vectors should be used for model training, after which the model learns to produce the best predictions.
Data labeling enables AI and machine learning systems to thoroughly grasp real-world surroundings and circumstances. Image annotation is a way of annotating images containing objects of interest to make them identifiable to machines. However, annotating data at this size is costly, time-consuming, and tedious. For example, image recognition applications need bounding boxes to be drawn around the objects of interest, and large datasets would mean labeling huge numbers of images at once. Thus, labeling data for computer vision is difficult since several approaches are used for image annotation to train algorithms that can learn from data sets and anticipate the results.
a. The Semantic segmentation technique classifies pixels in an image into semantic classes. The pixels belonging to a specific class are classified into the same category without considering any additional information or context. E.g., an image of a busy street would have a semantic segmentation model predicting all the four-wheelers on the road as belonging to the "vehicles" or "automobiles" class, without mentioning any detail or information on the image.
b. The Instance segmentation technique enables the identification of all the objects present in an image using their characteristics such as position, quantity, and size or form for segmentation.
c. The Panoptic segmentation technique allows combining the Semantic and instance segmentation techniques to visualize the data from all perspectives. Hence, it provides labeled data for semantic (background) and instance (object) types.
Let us explore the details of Semantic Segmentation in the following sections.
The implementation of computer vision applications has been evolving rapidly in recent years. Highly complex applications such as self-driving cars, geospatial intelligence, medical imaging, and Virtual Reality (VR) in retail are changing the face of these industries with AI. However, image processing in machine learning is a highly challenging task. Computer vision applications employ deep learning models that require large, high-quality, well-labeled image datasets for training and validation. Image annotation is expensive and requires a lot of time. This is where image and video segmentation are required to annotate the images.
Semantic Segmentation is an important image annotation technique that answers questions such as 'what is in this image?', 'where are the identified objects in the image located?' Segmentation allows arranging the data present in the images and videos into relevant classes.
Semantic segmentation finds its applications in several real-world scenarios related to images and video for image manipulation and 3D modeling, such as -
The process of Semantic Segmentation for labeling data involves three main tasks -
The Semantic Segmentation task can be classifying a specific class of objects in an image and then separating it from the rest of the other objects in the image using a segmentation mask. The main objective of Semantic Segmentation is to process an image to generate a segmentation map as output containing pixel values from 0 to 255 of the input images. These values are then transformed into a class label value (0, 1, 2, … n). Convolutional Neural Networks (CNN) are commonly used to carry out this task in most computer vision applications. It is important to note that for semantic segmentation, the aim is to extract features from an image before using them to divide the image into multiple segments.
In this article, we discussed the significance of data labeling in supervised machine learning applications. We explored the semantic segmentation method for data labeling and why it is crucial in deep learning applications like image recognition and object detection.
In conclusion, computer vision models may better interpret the content of an image when trained on suitably labeled data. Thus, image annotation is necessary for machine learning models to provide correct prediction outcomes and search results. Semantic segmentation is a preferred technique for image data labeling for building highly accurate computer-vision applications. Several open-source and commercial tools are available for image data labeling, but companies must cautiously choose a tool befitting their application requirements. Sometimes, annotating image data using semantic segmentation requires specific skillsets and expertise related to the industry where it is used, e.g., in the case of medical images.
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