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Image Segmentation

Kishan Kumar

Senior Consultant CRD(Corporate function) at Huquo

发布日期: 2022年11月17日

Image Processing or more specifically, Digital Image Processing is a process by which a digital image is processed using a set of algorithms. It involves a simple level task like noise removal to common tasks like identifying objects, person, text etc., to more complicated tasks like image classifications, emotion detection, anomaly detection, segmentation etc.

With the growth of Artificial Intelligence algorithms and its ecosystem, Digital Image Processing using Neural Networks has become popular in recent times. It has a wide variety of application areas like security, banks, military, agriculture, law enforcement, manufacturing, medical etc.

What is the Process of Image Segmentation?

A digital image is made up of various components that need to be “analysed”, let’s use that word for simplicity sake and the “analysis” performed on such components can reveal a lot of hidden information from them. This information can help us address a plethora of business problems – which is one of the many end goals that are linked with image processing.

Image Segmentation is the process by which a digital image is partitioned into various subgroups (of pixels) called Image Objects, which can reduce the complexity of the image, and thus analysing the image becomes simpler.

We use various image segmentation algorithms to split and group a certain set of pixels together from the image. By doing so, we are actually assigning labels to pixels and the pixels with the same label fall under a category where they have some or the other thing common in them.

Using these labels, we can specify boundaries, draw lines, and separate the most required objects in an image from the rest of the not-so-important ones. In the below example, from a main image on the left, we try to get the major components, e.g. chair, table etc. and hence all the chairs are colored uniformly. In the next tab, we have detected instances, which talk about individual objects, and hence the all the chairs have different colors.

Types of Image Segmentation

1. The Approach

Whenever one tries to take a bird’s eye view of the Image Segmentation tasks, one gets to observe a crucial process that happens here – object identification. Any simple to complex application areas, everything is based out of object detection.

And as we discussed earlier, detection is made possible because the image segmentation algorithms try to – if we put it in lay man’s terms – collect similar pixels together and separate out dissimilar pixels. This is done by following two approaches based on the image properties:

1.1. Similarity Detection (Region Approach)

This fundamental approach relies on detecting similar pixels in an image – based on a threshold, region growing, region spreading, and region merging. Machine learning algorithms like clustering relies on this approach of similarity detection on an unknown set of features, so does classification, which detects similarity based on a pre-defined (known) set of features.

1.2. Discontinuity Detection (Boundary Approach)

This is a stark opposite of similarity detection approach where the algorithm rather searches for discontinuity. Image Segmentation Algorithms like Edge Detection, Point Detection, Line Detection follows this approach – where edges get detected based on various metrics of discontinuity like intensity etc.

2. The Types of Techniques

Based on the two approaches, there are various forms of techniques that are applied in the design of the Image Segmentation Algorithms. These techniques are employed based on the type of image that needs to be processed and analysed and they can be classified into three broader categories as below:

2.1 Structural Segmentation Techniques

These sets of algorithms require us to firstly, know the structural information about the image under the scanner. This can include the pixels, pixel density, distributions, histograms, color distribution etc. Second, we need to have the structural information about the region that we are about to fetch from the image – this section deals with identifying our target area, which is highly specific to the business problem that we are trying to solve. Similarity based approach will be followed in these sets of algorithms.

2.2 Stochastic Segmentation Techniques

In these group of algorithms, the primary information that is required for them is to know the discrete pixel values of the full image, rather than pointing out the structure of the required portion of the image. This proves to be advantageous in the case of a larger group of images, where a high degree of uncertainty exists in terms of the required object within an object. ANN and Machine Learning based algorithms that use k-means etc. make use of this approach.

2.3 Hybrid Techniques

As the name suggests, these algorithms for image segmentation make use of a combination of structural method and stochastic methods i.e., use both the structural information of a region as well as the discrete pixel information of the image.