Image Segmentation using Deep Learning (Beginner Guide)

 

Image segmentation is among the main applications in the Computer Vision domain. This article is designed to give an easy-to-understand explanation of image segmentation as well as instance segmentation. Particularly, you'll be able to learn about:

What exactly is Image Segmentation?

The significance of Instance Segmentation

What are the most popular apps?

Semantic vs. Instance Segmentation

Most popular image segmentation datasets

What exactly is Image Segmentation?

A very significant processes of Computer Vision is Segmentation. The process of segmenting image data collection is the process of clustering the parts of an image which belong to the same class. This is also known as the pixel-level classification. This involves dividing the images (or videos) into several fragments or objects. Over the past 40 years, many segmentation techniques have been suggested that range between MATLAB image segmentation, and traditional computer vision methods , to modern deep learning techniques. Particularly with the advent of Deep Neural Networks (DNN) Image segmentation has made huge strides

The applications of Image Segmentation

Image segmentation plays a crucial function in a variety of real-world computer vision-based applications that include road signs detection, biology, evaluation of construction materials or surveillance video. In addition, autonomous vehicles as well as Advanced Driver Assistance Systems (ADAS) must be able to identify the navigable surface or use pedestrian detection.

In addition, image segmentation can be extensively used in medical applications, for example border extraction for tumors or measurement of tissue volume. This presents an opportunity to create standardized image databases which can be used to analyze the spread of emerging diseases as well as pandemics (for example, AI imaging applications for coronavirus prevention)

Deep learning-based Image Segmentation has been used successfully to split satellite images in areas of remote sensing. This includes techniques for urban planning as well as precision agriculture. Images gathered from drones (UAVs) were segmented with Deep Learning based techniques, providing the possibility of addressing significant environmental issues related to climate change.

Semantic vs. Instance Segmentation

Image segmentation can be thought of as a classification challenge of pixels that have the semantic label (semantic segmentation) or partitioning of distinct items (instance segmentation). Semantic segmentation enables pixel-level labeling with a set categories (for instance trees, people sky, cars, etc.)) for all pixels in an image.

It's generally an even more difficult task as compared to image classification which assigns one label for all images or frame. Instance segmentation expands the range of semantic segmentation by identifying and defining all items of interest within an image.

Image Segmentation and Deep Learning

Multiple algorithmic methods for image segmentation have been devised. Prior methods used bundling based on histograms, thresholding, regions growing, k-means clustering or watersheds. But, the more sophisticated algorithms are built on graph cuts, active contours as well as conditional and Markov random fields as well as sparsity-based techniques. In the past few several years, Deep Learning models have added a brand new segment to models for image segmentation that offer incredible performance enhancements. Deep Learning based image segmentation models usually have the highest performance on benchmarks that are widely used which has led to the paradigm shift of the field.

Most Popular Image Segmentation Datasets

Because of Deep Learning models' success in a broad range of applications for vision it has led to an abundance of research focused on developing methods for segmenting images made using Deep Learning. There are numerous general datasets related with image segmentation. The most well-known image segmentation datasets include:

PASCAL VOC

The PASCAL Visual Object Classes (VOC) Challenge provides publicly available images as well as annotations. The PASCAL VOC is one of the most popular datasets in computer vision, with annotated images available for 5 tasks--classification, segmentation, detection, action recognition, and person layout.

A large number of well-known segmentation algorithms were evaluated using this data. For tasks involving segmentation, the PASCAL VOS supports 21 classes of objects: vehicle labels household, animals airplane bike, boat, bus, motorbike, car train, bottle, table, chair, sofa, potted plant, monitor, TV, cat dog, cow sheep, horse and a person.

Pixels are labeled background if they don't belong to one or these categories. The training/validation information of PASCAL VOC PASCAL VOC has 11'530 images with 27'450 ROI annotated objects and 6'929 segments.

KITTI

KITTI is a dataset that has been used for a long time. KITTI dataset is among the most well-known datasets for autonomous driving and mobile robotics. It includes hours of videos of traffic scenes that were captured while driving around the middle-sized city in Karlsruhe (on highways as well as in rural regions). On average, in each video it is possible to see up to 15 vehicles and 30 pedestrians can be seen. The primary functions of this data set are road detection the stereo reconstruction process, optical flow visual odometry 3D object detection as well as 3D tracking. The original data set does not provide the ground truth needed for semantic segmentation, however researchers have manually tagged parts of the data.

Other Datasets

There are many other data sets available to be used for image segmentation, like for instance, the SUN databases (16'873 completely annotated photos) Vision of Shadow detection/text data collection, Berkeley segmentation dataset, the Semantic Boundaries Dataset (SBD), PASCAL Part, SYNTHIA Adobe's Portrait Segmentation and the LabelMe images database.

What's Next?

In the past few times the image segmentation and instance segmentation techniques have seen significant advancements. Therefore, image segmentation has accelerated the development of applications that are real-world across all industries, including cancer detection, material detection at construction sites, and perhaps most significantly autonomous driving. If you've enjoyed our article, here are some suggestions: suggest:

Everything you should be aware of

Image Annotation Read more about

Face Detection, Object Detection, or a combination of both

Discover the real-time algorithm for detecting objects YOLOv3

Our guide to OID and. COCO - differences and similarities

How GTS can help you?

Global Technology Solutions understands the need of having high-quality, precise datasets to train, test, and validate your models. As a result, we deliver 100% accurate and quality tested datasets. Image datasets, Speech datasets, Text datasets, ADAS data collection and Video datasets are among the datasets we offer. We offer services in over 200 languages.


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