Image Classification: The Fundamentals



How does image classification work?


Image classification is a process of assigning a class label to an image. The class label could be the name of an object, or a descriptor of the scene. The process of image classification involves extracting features from the image, and using these features to determine which class the image belongs to.



 

Preprocessing image for image classification


Machines don't see the world the way we see it. To a computer, this image appears as an array of square blocks known as Pixels assigned with a numerical value which indicates the pixel intensity of the pixel.




For this color image, each pixel has 3 values because the color image consists of 3 channels, each representing red, green and blue. the



Let's take a look at the grayscale image, for a grayscale image or black and white image, each pixel has one value ranging between 0 to 255. This is how the computer sees images and extracts some useful features from the images such as color, texture, shape and edge.


 

What is Image Processing?


Image processing (IP) is a computer technology applied to images that helps us process, analyze and kind of manipulating the information that you get when you take a picture. Usually, it’s to enhance it so whether that means you making it more clear to be able to kindly analyze the information in there or enhancing it in an aesthetic way kind of like Instagram, Tiktok and Snapchat. In other words, it is a process of analyzing and manipulating images through code. So how we derived information from the image processing process? It uses a mix of maths and computer science so a lot of it is mathematical functions that manipulate the different pieces of the image information and we are going to use computer science with processing to kind of mix it all together and create our filter.


 

Image Classification




So, how do we work with image classification tasks? Fundamentally, when you are doing image classification, your system receives some input image, which in this case [Pause] we have some cute cat and cute dog images as an example and the system is aware of a predetermined set of categories or labels such as cats and dogs and In image classification, we have to assign class labels or categories to an image. The job of a computer is to look at the picture and assign it one of these fixed category labels.


It seems like an easy problem for humans to identify each of these pictures belonging to what category or label because your brain is hardwired to do some sort of visual recognition task. But for computers, this is actually a hard problem. So if we go back to how the computer sees images, the computer doesn’t get this holistic idea of this picture as a cat and dog. Otherwise, the computer sees these images as a gigantic set of arrays.

The set of arrays now turns into data. Those data are what actually train in machine learning algorithms. The most recent of algorithm used in image classification is Convolutional Neural Network (CNN).


 

Convolutional Neural Network (CNN)



Convolutional neural networks are a type of neural network that is used to classify images. They are made up of a series of layers, where each layer is made up of a series of neurons. The first layer of the neural network is the input layer, which is where the image is fed into the network. The second layer is the convolution layer, where the image is convolved with a filter to extract features from the image. The third layer is the pooling layer, where the image is downsampled to reduce the amount of information that needs to be processed by the network. The fourth layer is the fully connected layer, where the output of the network is determined.


 

Conclusion


Image classification is a process of assigning a class label to an image. This can be done manually or automatically. Automated image classification algorithms typically use features extracted from the image to represent the image, and a classifier to assign the class label.



 

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