Here is a tutorial that teaches what are the uses of TensorFlow in image recognition. Also, we will learn the importance of the process.
What Is Image Recognition And Tensorflow Process?
The process of TensorFlow is the first step that you have to do before you start the image recognition. And then, if you want to have a definition of an image.
It will be based on TensorFlow because this is the main process that can define an image. And then, after this process, you will have a lot of processes that are related to TensorFlow.
How does Image Recognition work?
After the process of TensorFlow, two uses are also important for image recognition. And they are convolutional neural networks and feature extraction.
Then, if you want to learn the process of image recognition further. You should understand these two processes because they are very important.
What Is A Convolutional Neural Network?
A convolutional neural network is one of the neural networks that are used to solve the issue of image recognition.
First, this neural network is used to create a specific model for each object. This means that it can separate each object from the others.
So this is very important for an object recognition system. Because it can help us to distinguish each object from another one by its characteristics.
The second benefit is that it can create a general model about all objects just by using a few examples.
Therefore, it is also called Deep Learning. Why? It is because deep means many layers and many examples during training.
The third benefit is that there can be multiple layers in this neural network. It is because there are many small neurons in every layer.
However, except at the final layer where there is only one neuron. Thus it makes it easy to train compared with other neural networks.
Such as Logistic Regression or Support Vector Machine ( SVM)
The fourth benefit is that this neural network “learns” by itself at both training sessions. Also, testing phases because it uses backpropagation algorithms for training and classification algorithms.
What Is Feature Extraction?
Feature extraction is a process that can help us to identify the features from images. For example, the color, the shape, the texture, and others. Then it uses a series of algorithms in this process.
There are two types of feature extraction methods in image recognition. The first one is a direct method and the second one is a reverse method.
If you want to know how feature extraction works. You have to learn about these two methods. Also, you have to know why they are important for image recognition.
What Is A Direct Method In Feature Extraction?
In this method, we will use the raw pixels from an image as to features. Then we will compare each pixel with all other pixels to create a vector field.
It means that this process creates a unique vector for each pixel in an image or feature map. And then, there is an algorithm called Locally-Connected Convolutional Network.
So that can use these vectors to classify images by their characteristics. In this method, we will use the raw pixels from an image as to features.
What Is A Reverse Method?
In this method, we have to compare an image with a trained image. In other words, we have to compare the input image with the output image.
Then, this process will help us to identify the features from an image. The features can be features from color, shape, texture, and others. And then each feature will have a vector field.
So that can be used to classify images by their characteristics. In this method, we will use the raw pixels from an image as to features.
