Facebook and several other social media store billions of image data. Daily, users upload up to 350 million new images on the platform. The social network could be heading to petabytes or even exabytes of data which mostly comprise images. But images are not just important for social networks, they are equally relevant in every business. In fact, with appropriate analysis and use of image data, businesses can improve their productivity and growth.
Learning and Images
is one of the most applicable areas of machine learning. As the name implies,
it involves recognizing objects in an image using some machine learning
algorithm. To achieve recognition, the objects have to be localized and
possibly identified. Recognition is often achieved by comparing an identified
object with objects already existing in the database.
While the field of
machine learning is extremely technical, several tools have been made available
to improve the accuracy of image recognition. Deep learning even makes the
process even more efficient. With frameworks like Tensorflow, Keras, Pytorch,
etc, it is possible to build a robust image recognition algorithm with over 99%
image recognition will have a remarkable impact on the way we live, drive and
move. The future of vehicles would be completely redefined by machine learning,
especially image recognition. Self-driving (driverless) cars mostly apply the
YOLO algorithm to identify objects and adequately classify them. This allows
the vehicle to spot objects and pedestrians and therefore drive safely.
Google, Tesla and Uber are pioneers in the driverless technology. With computer
vision, these technology giants have developed driverless cars that could
understand traffic signs and even communicate with each other through sensors.
Management in Businesses
is an essential part of any business that ensures the smooth running of the
business. One of the most essential aspects of process management is the
identification process during business operations. Manual identification is
adopted in most traditional businesses; this involves the use of ID cards to
allow entry and exit.
companies are gradually adopting image recognition for process management and
identification. For instance, rather than using the traditional ID-card system,
Baidu adopted face-enabled entrance to recognize staff members and let them in.
This reduces fraud and makes the process much more efficient.
and Social Media
is mostly used by e-commerce companies for search and advertising. Powered by
deep learning, image recognition can also be used for customer-centric
searches, marketing and analytics, social media commerce, and so forth. By
labeling features of images and videos, the algorithm can search and organize
content and, sometimes, the system can be trained to understand and identify
images and logos. With image recognition, marketers can even fine-tune and
adjust their campaign by monitoring the users’ expressions and emotions. This
will help them to achieve a better result in the campaign, getting more return
on their investments.
Social media has
also benefitted enormously from image recognition, Facebook is a notable
example of this. For instance, Facebook’s image recognition algorithms can
recognize friends and families and even identify their names and other useful
attributes. Several other social networking platforms such as LinkedIn and
Twitter also make use of object detection and image recognition. Image
recognition in social media does not just help users to locate their friends
and loved ones but it also searches easy and effective. Aside from social
networks, a lot of other image recognition apps exist; notable examples include
Google Vision AI, IBM Image Detection tool, Amazon Rekognition, Google Image
Recognition, to mention a few.
Robotics and AI go
hand in hand. With advances in image recognition, robotics and IoT have taken a
new turn. Machine vision allows industrial robots to locate objects precisely
and even ensure that collision is completely avoided. This ensures efficiency
and precision in industries where automation is required. Reinforcement
learning has even taken robotics to a whole new level as machines can be
programmed to learn about their environments and things on their own. This
requires the use of sensors and 3D imaging system to capture objects, image
recognition is then applied to identify, localize and recognize objects.
Image recognition also finds application in surveillance and security. Facial recognition is applied by security companies and police departments to identify crime. This usually involves scanning through millions of images and feeding them into deep neural networks. The deep learning system analyses the images and compares them to suspects. Video surveillance may also be used in some cases as it has proven to be very effective in detecting and tracking crime incidence.
industry is one of the biggest industries today, thanks to top gaming
applications like Xbox One. Nowadays, machine learning and especially image
recognition has made the gaming industry even better. For instance, Xbox One
now uses facial recognition and it works well with Microsoft’s 4K webcams. The
technology is also applied in other aspects of gaming which when combined with
the advanced sensing capabilities of most gaming applications produce a whole
new level of experience.
Image analysis is
an important aspect of medical studies. It involves analyzing body parts to
detect and identify diseases. In the era of deep learning and machine learning,
image recognition has been used to improve image analysis for medical purposes.
Several machine learning image processing algorithms can be used for this
purpose such as the k-means algorithm, ROI-based segmentation and so on.
Furthermore, deep learning algorithms can be employed to achieve very high
accuracy, much more than professional medical practitioners can achieve.
image recognition has a lot of applications in industries and businesses from
driverless cars and ecommerce to industrial automation and medical image
analysis. Technology has played a huge part in the fourth industrial revolution
as it is applied in most business organizations to improve searches and