Object recognition vs Image Recognition

“I visualise a time when we will be to robots what dogs are to humans, and I’m rooting for the machines.” — Claude Shannon

 

No wonder with the recent technological advancements, a time will come soon when we will feel inferior to our own inventions. AI has achieved many milestones, all of which are due to the human brains that work day and night. 

 

AI in itself is a broad concept. It has a variety of fields in it. A lot of research and analyses are done each time something new is being developed. What we possess is being transferred to machines. 

 

Human intelligence is the biggest asset. And now the experts are hell bent on transferring it to the machines. The human touch is what machines lacked, and now that touch is what experts want machines to have.

 

AI has opened so many doors for us to make everything a possibility. Imagine machines with the capability of overcoming all the hurdles and tasks along with human feelings and touch. If that happens (which soon will) would you want to make human interactions? Would we ever need humans? What will happen to human labour? 

 

We all have to think about this. In this blog you will understand two important concepts in AI called “object recognition” and “image recognition”. And learn how they are almost indifferent to human eyes.

 

(Recommended blog - Strong AI)

 

 

What is Object Recognition?

 

Object recognition is a technique of identifying objects in the videos and images. It is a significant output of machine learning and deep learning programs. Humans have the gift of sight to identify anything and everything in their surroundings. 

 

But machines don’t. Object recognition aims to achieve this goal. With this techniques machines will be able to identify images like humans. For humans it’s natural but for machines it is a process to learn. Not only identifying the image but with object recognition machines can understand what the image contains.

 

Both object detection and object recognition are similar. But the only difference between the two is that they are executed differently. Currently the most popular object recognition tools are YOLO and Faster RCNN. 

 

Like mentioned above, object recognition is the key output of machine learning and deep learning. Let us look at both these techniques as well. 

 

• Machine learning

 

It offers a completely different outlook than deep learning. Examples of this method are - HOG, Bag of words model, Viola jones algorithm

 

• Deep learning

 

Object recognition can be done in 2 different ways when using deep learning. Let us see how- First way is to train the model from the start. It means you start from scratch and work your way. For this you gather a dataset and then build an architecture based on it. 

 

This approach can give very impressive results but the amount of time required will be very much. The second way is to use a transfer approach. In this you don’t start from scratch and use a pertained model. In it less time will be required and results will be immediate.

 

(Recommended read- Deep learning vs machine learning)

 

Applications of object recognition

 

1. Tracking object

 

Object recognition can be used to track objects. Tracking a ball during a cricket match when batsman hits a six, an individual in any video, Football during world cup.

 

2. Counting people

 

Object recognition can be used for people considering the fact that people are non-flexible objects. Otherwise it will be difficult to do it.

 

3. CCTV surveillance

 

It is one of the best uses of object recognition in real life. Security, surveillance and tracking people is also possible. Surveillance systems are programmed to find criminals, people doing any misconduct or even for kidnapping.

 

4. Vehicle Detection

 

In order to find the vehicles, checking their speeds, running their number plates for security checks can be done through object recognition.

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Object Recognition

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 “We will be using the term object recognition broadly to encompass both image classification (a task requiring an algorithm to determine what object classes are present in the image) as well as object detection (a task requiring an algorithm to localize all objects present in the image)”

— ImageNet Large Scale Visual Recognition Challenge, 2015.

 

Process

 

From object recognition to image segmentation the process undergoes as follows: First either image classification or object localization is carried out. Both of which result in object detection and ultimately image segmentation. Here image classification is the same as image recognition. Let us look at these steps in brief -

 

• Image classification- To distinguish between classes of objects that are in the image.

 

• Object Localization- It is about locating the presence of objects and their length, breadth, width by the box.

 

• Object Detection- List of objects in the picture are prepared using the algorithms.

 

(Related blog - Facial Recognition)

 

 

What is image recognition?

 

Just like object recognition, image recognition is also a technique wherein machines are programmed to identify people, places, actions and objects. AI and camera are combined for image recognition. Computers can also use technology for doing the same.

 

Many famous companies like Google, Facebook, Microsoft, Apple, Pinterest are investing a huge sum of money for image recognition techniques. Image recognition for humans and animals takes no effort but for computers it is difficult. Therefore, programs are developed through deep learning.

 

(Learn more about Deep Learning through our blog) 

 

Applications of image recognition

 

1. Drones

 

Drones are fitted with image recognition programs that can help in detection, supervision and inspection in remote areas.

 

2. Manufacturing

 

To monitor the progress of products to minimize the damages, to check on products when they are in assembly lines, to overlook workers in factories, image recognition can be very helpful.

 

3. Forest surveillance

 

To monitor the forests, changing patterns, animal hunting and poaching prevention; image recognition can be very useful.

 

4. Military surveillance

 

In order to detect unusual activities on the border, check infiltration etc. this technique can be beneficial.

 

5. Medical purposes

 

Image recognition techniques are very useful in medical treatments. There are many underlying diseases like Melanoma, it is a type of skin cancer. With image recognition the growth of tumor can be checked. 

 

Other diseases like breast cancer can also be detected. Many anomalies in the body can be detected as well.

 

(Suggested Read - AI in Cancer Detection and Treatment)

 

 

Conclusion

 

If we look at image recognition techniques from a business point of view then it can provide businesses with what they desperately need. Also, it can offer them insights about the future. 

 

Marketplace can be better understood using image recognition. Manufacturers and retailers are using it to better understand their clients. Accurate and reliable data can be obtained using this technique.