Image Classification

1. What is Image Classification?

Image classification model is a model that classifies different images into different classes by identifying what is in the image. If an image of a sidewalk and an image of a driveway were given, an Image classification model would classify each image into appropriate classes. The following image would be classified 'driveway.'

Image classification is an easy task for human. But to classify multiple images at once, it would be a wiser decision to have a computer classify images. And a technique called convolutional neural network is used to make this possible.

2. Convolutional Neural Network

Convolutional neural network is a type of deep learning that is used to find patterns using spatial information. Convolutional neural network uses convolutional layer, maxpool layer, and dense layer in its learning process, but the key is in the convolutional layer. Convolutional neural network collects spatial information through the convolutional layers' filters(also called kernels), and the training of the layer has a great impact on the network's performance. To make it easier to understand, we will look at an image that briefly explains the role of a convolutional layer.

If the blue table(5x5) is the input for the convolutional layer and the green table(7x7) is the output, the moving grey table(3x3) is the filter. This system is named convolutional neural network, as this process is called convolution. The convolution is calculated as the filter and the inner product of the area.

For example, the image above could be calculated with the equation above.
Because the extracted features can vary depending on the value of the filter, the convolutional neural network improves the filter value by reducing errors throughout the learning process. The filter in the example above is a filter that detects vertical edges. As more correct filters and layers accumulated, more complex features can be extracted. For example, abstract features are extracted through steps like 'vertical edge → road lane → driveway.'

3. Image Classification Examples

1. Autonomous driving development
2. Predicting disease prognosis using biomedical images
3. Face recognition

4. Image Classification with CLICK AI

CLICK AI provides codeless artificial intelligence model development experience for users. With a just few clicks, users can develop image classification models at a professional level through the automated process.

When model development is complete, you can classify your images like in the image above. The input image is shown on the left, while the classification result is shown on the right.
You can start developing an image classification model by clicking 'Develop AI' from CLICK AI's main page. More information about image classification model development is available at 🖼️이미지 분류.

References
https://towardsdatascience.com/image-classification-in-data-science-422855878d2a

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