As humans, we can distinguish between different objects easily - such as dogs wearing hats, or between oranges and bananas in a bag - but for computers this has been typically much more difficult. Until now.

A team of Google researchers has developed an advanced image classification and detection algorithm called GoogLeNet, which is twice as effective than previous programs.

It is so accurate it can locate and distinguish between a range of object sizes within a single image, and it can also determine an object within, or on top of, an object, within the photo.

A team of California-based Google researchers developed GoogLeNet, that uses an advanced classification and detection algorithm to identify object. The technology is so accurate, it can distinguish a range of object sizes, and it can also determine an object within or on an object – such as a dog wearing a hat (pictured)

The software recently placed first in the ImageNet large-scale visual recognition challenge (ILSVRC).

GOOGLENET'S IMAGE ALGORITHM

The Google researchers began by training neural networks to carry out the recognition tasks similar to how the human brain works. The layers and structure of the algorithm were based on the Hebbian principle, for example, which describes how neurons adapt in the brain as we learn. It also features scale invariance, which states that objects don’t change even if they’re multiplied by a common factor. For example, at its most basic, making an image of an object larger or smaller doesn’t change the object, its shape or proportions. This helps the software learn the shape and size of different objects, no matter how small, and be able to recognise them in the future. A more detailed explanation is available from the Google Research Blog.



This annual academic challenge was set up to test state-of-the-art technology in image understanding, both in the sense of recognising objects in images and locating where they are.

Google has made the software open to other developers, to help increase its accuracy, and in the future, the technology could be used to improve Google Image searches.

It could also scour YouTube videos for specific objects or shapes.

The competition has three categories, including classification, classification with localisation, and detection.

The classification track measures an algorithm’s ability to assign correct labels to an image.

The classification with localisation category assesses how well an algorithm finds an object within an image, and how accurate its label is.

Finally, the detection challenge is similar, but uses stricter evaluation criteria.

As an additional difficulty, the challenge includes images with tiny objects that are hard to recognise and locate, even by the human eye.

The software recently placed first in the ImageNet large-scale visual recognition challenge (ILSVRC).This annual academic challenge was set up to test state-of-the-art technology in image understanding, both in the sense of recognising objects in images and locating where they are. Examples are pictured

The researchers began by training neural networks to carry out the tasks similar to how the human brain works. This was based on the Hebbian principle, which describes how neurons adapt in the brain as we learn. This helps the software learn the shape and size of different objects, no matter how small (examples pictured)

To score highly in the challenge, an algorithm must be able to describe a complex scene by accurately locating and identifying all the multiple objects in it.

In this year’s challenge, team GoogLeNet doubled the quality of last year's results.

The algorithm was created by Google interns Wei Liu and Scott Reed, as well as Google researchers, Yangqing Jia, Pierre Sermanet, Scott Reed, Drago Anguelov, Dumitru Erhan, Andrew Rabinovich, and software engineer Christian Szegedy.

The researchers began by training neural networks to carry out the recognition tasks similar to how the human brain works.

The layers and structure of the algorithm were based on the Hebbian principle, for example, which describes how neurons adapt in the brain as we learn.

It also features scale invariance, which states that objects don’t change even if they’re multiplied by a common factor.

In this year’s challenge, team GoogLeNet doubled the quality of last year's results. The technology built upon image recognition research carried out by Alex Krizhevsky from the University of Toronto, which was able to teach computers to divide images into objects (pictured)

For example, at its most basic, making an image of an object larger or smaller doesn’t change the object, its shape or proportions.

This helps the software learn the shape and size of different objects, no matter how small, and be able to recognise them in the future.

A more detailed explanation is available from the Google Research Blog.