Detection in Image Recognition

Image recognition is very important technology and image recognition has an endless scope in every field. Image recognition techniques have been applied in different applications such as finding pedestrians on the streets, detecting moving vehicles, counting the people in public places. Application of image recognition in the medical field has also been proven significant.

Image recognition technology is usually evaluated with the detection rate which indicates how well the objects are detected. The detection rate is measured based on the test where the number of successfully detected images are counted among the images used as input for the detection algorithm.

For example, let's say we have 100 images with faces of people. Each image may have either only one face or many faces. So let's say that the total number of faces included in 100 test images is 200. If the number of faces detected by the face detection algorithm is 100, the detection rate of this algorithm 50%.  Generally speaking, the detection rate of 50 percent doesn't sound appealing but it is not always the case. For example, let's say we have a CCTV camera that detects the faces of the people passing by. Let's assume that this camera takes footage at 10 frames/second and use it for face detection. So whenever a face appears in front of the camera, the same algorithm with 50% detection rate will detect it with 99% chance of detecting it.

Although detection rate is a reasonable measure for evaluating the performance on still images, for evaluating the performance of the real-time video, detection rate per unit time is more appropriate metric.

Machine Learning

Machine Learning

There are several keywords being used quite frequently in tech world like, Big data, Machine Learning, Information Discovery, Data Mining etc. Although Machine Learning is being practiced for a long time it has been one of the top buzz words in the tech industry in past few years.

Since data is at the center of all technologies, Machine learning is being used by scholars, enterprises of all domains. So such keywords are being used in different domains in different way but we can group them under the umbrella of Artificial Intelligence. 

Abundance of Data,

Data is being collected from everywhere for example, social network data, news articles, purchase data, consumer response data in various field, health related data, similarly various device generated data is being collected in large quantity.

A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E. -- Tom Mitchell

Machine Learning is Everywhere

Document classification

Document classification has been one of the widely used application of Machine learning  for quite some time now. Spam detection in emails has been prominent in the email service for quite a long time.  Classification of online information contained in blogs, news articles etc. and  information extraction is also an important application of Machine learning.

Stock market prediction

Although the stock market prices are said to be highly un-predictive in nature, but with the right approach and use of proper features selection has enabled various firms to leverage machine learning technique to make useful prediction of stock market prices.

Character recognition

Character recognition is another important application of Machine learning. Now the Optical character recognition is sophisticated enough to correctly identify the wide range of printed and handwritten characters which has been very useful in day-to-day life.

SNS recommendation

Similarly, recommendation systems in social networks, like Linkedin, Facebook are amazing application of Machine Learning. In social networks use of machine learning is widespread for recommending related contents to the users. Providing the relevant feed to the users is one of the most important aspects of social networks.

Robotics

Robotics is another field where machine learning is widely used which involves various decision making and computer vision. Imitation learning techniques have been very important part of robotics. Machine learning has made robotics an integral part of various domains like medicine, Transportation, and manufacturing industry.

Random Jungles

After a long time, I thought I should continue blogging again and started this blog to share my experience with machine learning, which you might have guessed from the blog name, inspired by popular machine learning algorithm(Random Forests). Although I will be writing about data science most of the time, But this blog isn’t much formal so there will be some gossips about my personal experiences too. The blogs I share  will be mostly based on what I know about Machine learning so might not be explanatory enough. Please feel free to suggest any additional information through comments.
I also Imported Andorid related blogs from my old blog(Technoguff). I will continue writing about Android too whenever I am free.