From recognize the scene and its category

From the single glance a human can understand
world which is considered to be a great accomplishment. All it takes for seconds
to categorize certain environment or an object and its importance in the field
of scene recognition. One of many human capability is to learn and memorize
places by analyzing the world in some seconds. Our neural design constantly
saves different input even for a short span of time. It is common that people
can recognize scene they see such as a mosque, tomb or any fort. The recent researches
show that in 36 milliseconds processing time with 80% accuracy viewer can
recognize the scene. Now question raises how much an artificial machine will
learn before stretching out possession of human being and how we can recognize
scene that fast and what information do we need?

It is important to understand scene perception because in researches
it if found that a scene uses our certain knowledge that is connected to some
scene category (eg, mosque, tomb, fort). Such knowledge forces that we must pay
attention and it may be helping in recognizing a particular object and determines
what kind of information do we need to memorize from the scene. Researchers on
scene recognition travel a point between two system i.e cognition and
perception and it is a problem that it considered to be a challenging task for
a person working in artificial intelligence. This kind of researches can be
used to design a system in artificial intelligence that have the ability to
recognize the scene and its category

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A CNN is a class of deep learning and feed
forward AI that has been successfully applied in scene recognition and visual
imagery. Convolutional neural network directly learn
from image data set and removing the need to manually feature extracting to
produce better recognition result. CNN are much like ordinary neural and CNN
consist of multilayers that is used to minimal preprocessing. CNN is also known
as shift variant. Conventional neural network appears to be successful in many
real life studies and application such as image classification, face
recognition and much more. We will have to go back 2012 to understand its
success when Alex Krizhevsky
used CNN to win 2012 imageNet event, resulting in error reduced from 26% to 15%

 

A convolutional neural network may even have hundred
layers to discover different features in an image. CNN
architecture is design to take 2D structure advantage. It is achieved by local
connection and tied weight followed with certain pooling that result the
invariant translation feature. Additional advantage of conventional neural
network is that it is trained easily and require few parameters that can
connect fully operational network with identical no. of hidden units. CNN uses
a variation of multilayer
followed by full connected layer designed
to require minimal preprocessing. CNN
networks is designed similar to biological processes in
which the connectivity pattern between neurons is
inspired by the organization of the animal visual cortex. In
CNN every neuron will be receiving input and carry out a dot product followed
by non-linearity. There
are three kinds of layer in CNN input, output, hidden. The hidden layer contains
pooling, convolutional, fully connected and normalized layer