Machine Learning and artificial intelligence (AI) are two technologies that are rapidly transforming many industries as they represent an important evolution in computer science.
A growing tsunami of data confronts businesses and organizations as they undergo digital transformation. This data is both valuable and difficult to collect, analyze, and process. According to the market research reports of the International Federation of Market Research (IFMR), the global digital transformation market is predicted to reach $1,009.8 billion by 2025, with a compound annual growth rate (CAGR) of 16.5% during this period. The vast amount of data that is being collected requires new tools and methodologies so that it can be managed, mined for insights, and acted upon once those insights have been discovered.
Object detection is the foundation of image segmentation, which builds on this premise. Object detection (and other processes related to it) and image segmentation have significant similarities and differences. Before beginning, a clear understanding of what segmentation in image processing entails is necessary. Segmenting an image is the process of labeling each fragment of an image once it is segmented. This process defines objects within a frame and their classes on a pixel level. Depending on the type of segmentation, these outlines – called outputs – are highlighted with either one or more colors.
For machine learning, applications of image segmentation are streamlined by training the system with sets of data – be they manually collected or open-source – so that visual inputs can be accurately identified and labeled.
There are two basic types of image segmentation that can be categorized as follows:
Segmenting images can be accomplished using classical as well as traditional techniques. A video or image’s final output is determined by the method used by each image segmentation technique. There are various techniques for segmenting images, including region-based segmentation, threshold-based segmentation, and edge detection segmentation. Here’s an in-depth look at some of them.
The threshold technique is probably the least complex method of image segmentation techniques that can be used. In essence, it defines ‘black-and-white’ as the process of converting an image from a color image to a black-and-white image that will result in a binary image or binary map as opposed to the original image. There are typically two values for pixels in rendering, 0 and 1. Usually, the pixels are assigned a value of 0 for the background and a value of 1 for anything above the threshold in the foreground. An effective way of enhancing a photograph is through this technique, which is perfect for images with a strong contrast between the background and foreground.
In this technique, similar pixels in segments that are in close proximity to one another are compared to detect similarities between the segments. This technique is based on analyzing the similarities and differences between adjacent pixels in order to determine the boundaries of the given object based on the likelihood that the pixels closest to each other are part of the same object. This is why same-pixel pixels are more likely to be part of the same object. Lighting and contrast within the image may cause inaccurate defining of the object parameters within the image, which is one of the shortcomings of this technique.
By emphasizing object edges to achieve reliable results, edge detection algorithms were developed to resolve the shortcomings of region-based techniques. Certain pixels need to be classified as “edge pixels” before anything else can be done to identify them. As opposed to other image segmentation techniques that require more time to implement, edge detection is ideally applied to visuals with clearly defined outlines and is simple to implement, and can be used for regular use.
In recent years, deep learning has become the most accurate method for segmenting images. A basic understanding of how segmentation with deep learning works is sufficient if you are interested in executing image segmentation for machine learning.
Encoders and decoders segment images. This process extracts parts of an image via filters in the pooling layers, and a segmentation mask is then applied to the final output. Encoder-decoder architectures that use convolutions are also known as convolutional encoder-decoders. Convolutional neural networks resemble ‘U’ shapes when visualized, which makes U-Net one of the most notable models. The process consists of two parts, an upsampling path, and a downsampling path. Using the same feature maps that were originally used to expand a vector into a segmented output image, U-Net achieves accuracy and speed for image segmentation. Image segmentation for medical imaging is the most prevalent application of the U-Net architecture.
In computer vision, the three most common types of annotations can be compared to understand the primary benefit of image segmentation better:
So, now you might have a clear view of image segmentation, its techniques, and its types. If you’re willing to learn more about the field, you are recommended to pursue the UNext Executive PG Diploma in Management and Artificial Intelligence in association with IIM Indore.