Why Is CNN good for image recognition? It is effective for image recognition and also the concept of dimensionality reduction.
What Is Image Recognition?
Firstly, let us have a small insight into what is Image recognition. So image recognition is natural for humans and animals. However, it is a difficult task for computers to perform.
In computer vision, it uses artificial intelligence (AI) technology to identify the following:
- Objects
- People
- Places
- Actions in image
Also, Image recognition is used to complete the tasks. Such as:
- Labeling Image with detailed tags
- Searching Content in image
- Guiding autonomous vehicles, robots, and driver assistance systems.
Over a couple of decades, the field of computer vision has developed. As a result, it emerged with other tools and technologies.
For example:
- Drones – provides automatic vision-based such as: monitoring, inspection, and control of assets
- Manufacturing – inspects production lines and evaluating critical points within the premises.
- Autonomous Vehicles – it identifies activities on the road and takes necessary actions.
- Military Services – It detects unusual activities in the border areas. Also, helps prevent infiltration and saving soldier’s lives.
- Forest Activities – Unmanned Aerial vehicles can monitor the forest and predicts the changes.
It is a kind of upgrade. However, it arises challenges.
The Challenges Of Image Recognition
- Viewpoint Variation. In actuality, the image is aligning in different directions. If such an image operates in the system, the latter part will predict inaccurately.
- Scale Variation. The modifications of sizes affect the classes of the object. It’s like the more you zoom in the bigger it looks like.
- Deformation. We all know that in actual, the shape changes or deforms base on the objects. However, the system only learns from the perfect image. Also, it forms specific shapes only.
- Occlusion. Some objects obstruct the full view of an image. It may result in sketchy information being served in the system.
- Inter-class Variation. Objects may differ within the class. Such as different in shape, size but still belongs to the same class.
Here is an example:
- Buttons
- Chairs
- Bottles
- Bags
What is CNN?
Convolutional Neural Network or CNN for short, is the topmost effective tool found for the task of image recognition.
Furthermore, it plays a crucial part in solving the problems listed above.
CNN serves with a complete image. This image is divided into small sets. Moreover, each set serves as an image.
These small sizes of filters distribute the complete image into small sections. Every section has a set of neurons that are connected.
So Why Is CNN For Image Recognition?
CNN may have shortcomings in its utilization. For example, in Data set up to large parameters, it requires a lot.
Such as,
- High computation load
- memory usage
- high processing power.
CNN is truly outstanding. It evens surpasses the human 94% capabilities by a 95% accurate in predicting image.
Thus, CNN decreases computation power requirements. Also, it allows the treatment of large size images.
It is delicate to variations of an image. Therefore, it results or provides higher accuracy than any regular network.
