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rakesh kumar
rakesh kumar

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Difference between object detection and image segmentation in deep learning

Define Term
image classification
object detection
image localization
image segmentation

In the computer vision field, one of the most common doubt which most of us have is what is the difference between image classification, object detection and image segmentation. When I started my journey in the computer vision field, I was also confused with these terms. So, I decided to break down these terminologies which will help you to understand the difference between each of them. Let’s start with understanding what is image classification:

Consider the below image:

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You will have instantly recognized it. It’s a dog. Take a step back and analyze how you came to this conclusion. You were shown an image and you classified the class it belonged to (a dog, in this instance). And that, in a nutshell, is what Image Classification is all about.

As you saw, there’s only one object here: a dog. We can easily use image classification model and predict that there’s a dog in the given image. But what if we have both a cat and a dog in a single image?

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We can train a multi-label classifier, in that instance. Now, there’s another caveat - we won’t know the location of either animal/object in the image.

That’s where Image Localization comes into the picture. It helps us to identify the location of a single object in the given image. In case we have multiple objects present, we then rely on the concept of Object Detection. We can predict the location along with the class for each object using OD.

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Before detecting the objects and even before classifying the image, we need to understand what the image consists of. This is where Image Segmentation is helpful.

We can divide or partition the image into various parts called segments. It’s not a great idea to process the entire image at the same time as there will be regions in the image which do not contain any information. By dividing the image into segments, we can make use of the important segments for processing the image. That, in a nutshell, is how Image Segmentation works.

An image, as you must have known, is a collection or set of different pixels. We group together the pixels that have similar attributes using image segmentation:

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By applying **Object Detection **models, we will only be able to build a bounding box corresponding to each class in the image. But it will not tell anything about the shape of the object as the bounding boxes are either rectangular or square in shape.

Image Segmentation models on the other hand will create a pixel-wise mask for each object in the image. This technique gives us a far more granular understanding of the object(s) in the image.

I hope you now have a clear understanding of what is Image Classification, Image Localization, Object Detection and Image Segmentation. To quickly summarize:

Image Classification helps us to classify what is contained in an image. Image Localization will specify the location of single object in an image whereas Object Detection specifies the location of multiple objects in the image. Finally, Image Segmentation will create a pixel wise mask of each object in the images. We will be able to identify the shapes of different objects in the image using Image Segmentation.

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