Deep learning in image recognition has made great success. Still a lot ask, why is image recognition hard?
Are you one of them asking this question? Do not worry about it. In this context, we will find out the main reason behind this.
Why Is Image Recognition Hard?
Before we dig into what is considered to be the dominant factors. We need to check the explanation of how machines see images.
We, humans, look at an image, we see things, people, or a scene. But it is different in machines. Because when they “look” at images, all they see are numbers. Representing the individual pixels.
So, it needs to process these numbers in one way or another. For a machine to take anything about an image.
Since we already know the essential background information. Let us now move to the main reasons. We will look at it one by one.
Top Main Reasons
#1. Dealing with a lot of data
As we already know, when machines look at images, they see numbers. Not just one number. But a lot of numbers. Which means a number of data needs to be processed. Why? To make sense.
#2. Losing of information
Loss of the information in the digitization process. Another major player adding to the trouble involved in image recognition.
The nature of image processing includes the end goal that will get you information from a 3D world. And then plays it in a 2D plane, like a flat image. So it also means that in this process, you lose a lot of information. Even though we still need to deal with a lot of data.
#3. Dealing with noise
The digitization process, often accompanied by noise. For example, no cameras can give perfect pictures. Especially to the cameras of our phones. Even the phone cameras are getting great with each new release.
The intensity levels, color saturation, and so on. These are just an attempt to capture our beautiful world.
#4. Requirements for interpretation
Interpretation, the last and more important. Certainly, the hardest one for a machine to manage. With regards to image recognition. So viewing an image it examines by the years and years. collected learning and memory.
Summary
So we already discussed the reasons why it was hard. We know the 4 major reasons as follows:
- Images are represented by a lot of data. That machine needs to process before retrieving information from them.
- When talking to images we are interacting with a 2D reality. That reduced from 3D. So it means a lot of lost information.
- Devices that show us the world often also deliver noise.
- And most importantly, interpretation. A drawback for machines. The inability to fully understand the world around us. As well as its complications. Which we have learned to deal with since the beginning of our lives.
But we still look forward to the advances coming soon. Also, look forward to these new technologies transforming our lives. In deep and amazing ways.
