Neural Networks For Image Recognition

Image Recognition & Convolutional Neural Network

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Neural Networks For Image Recognition. Image recognition technology is the task of identifying images and categorizing them in one of several predefined distinct classes.

So, image recognition software and apps define what’s depicted in a picture and distinguish one object from another. Also, this technology has extensive applications, which can be summarized in 5 types of application. Assisting in Education, Optimizing medical imagery, Boosting automated driving, predicting consumerism behavior, and Biometric identification. 

Image recognition is one of the tasks of neural networks that play an incomparable role in image recognition. Convolutional neural network(CNN) is the most common neural network used in this area, which can be found almost everywhere in image recognition from Facebook’s photo tagging to self-driving cars.

This neural network is inspired by the biological process, where each of mammals’ neurons is responding to only part of the whole image(the coverage of the neurons is called receptive field), and the overlapping receptive fields combine to become the entire image. And the structure of CNN basically follows the structure of mammals’ recognition pattern.

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Before we dig into the details of CNN, I would like to familiarize you with how image recognition works. Image recognition includes two key processes, the models take the input (like a photo of dogs), and one the models output the class or the prediction result of a particular class (there is 80% that it is a dog). CNN runs these processes with its own network structure. 

This type of network includes Convolutional Layers, ReLu layers, Pooling Layers, Fully connected layers, and sometimes dropout layers. In the beginning, the Convolutional Layers use filters to extract a smaller filter to extract partial information from the images.

And the results of these subareas end up as a feature map. In addition, are fed into the nodes in the next layers and the nodes will be activated with the ReLu function then feed their result to the next layers. With the deep connection of multiple convolutional Layers, pooling layers, and etc, the last layer is designed to be a fully connected layer that provides the final prediction of the objects.

And with a greater amount of training, for example. Photos and videos and more computational power, neural networks can now be built with a high number of iterations. Furthermore, higher number of layers and yield better results.

In conclusion, we would be able to foresee a wider usage of neural networks in image recognition.

Neural Networks For Image Recognition Written by Shen Lincong

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