The Ultimate A-Z Guide to Image Classification for Experts and Beginners

Technologies Used in Image Classification

Usually, technologies combining artificial intelligence and camera are utilized for it. Here are some of the leading technologies which aid image classification techniques:

1) HOG Technology

The HOG technology or histogram of oriented gradients is a tool utilized to detect objects which are found in a video or image using a feature descriptor. This tool moves a sliding detection window across the image to process the image. Then, the descriptor is processed by a support vector machine, which will then title the object as non-person or person.

2) HAAR Technology

The HAAR technology or High Altitude Aerial Reconnaissance is a tool utilized to recognize objects. This tool takes the adjacent regions in the detection window in a rectangular shape. Then, the tool adds the pixel intensities as calculated of each region and finds out differences in these sums.

Here are some of the HAAR tool features related to image classification:

· Edge feature which is utilized to find edges.
· Four rectangle feature which is utilized to find slanted lines.
· Line feature which is utilized to find lines.

3) Image Classification Using CNN

There’s always a motivating factor behind every data scientist who wants to understand image classification. This motivation will be your driving force. So, we have utilized this as the first step because it is hard to make a model without motivation.

2) Dataset

Once you are motivated to build an image classification model using CNN, pick your dataset. Your data or images must be of high-quality, as better quality images always help you retain accuracy in your model. If you are taking images from the internet, there is a high chance that your model will not perform as expected. For instance, it will show overfitting.

To solve this issue, you may either use the original source of images or videos or fix the overfitting problem. If you wish to fix this problem, you can decrease the epochs number, tune hyperparameters, etc. It is necessary to note that through these methods, we are trying to create a training set which is similar to our test set.

Further, you also need to upsample your set by data augmentation if you have more images from the source and less from the internet. In many case scenarios, you will have more samples from the internet and less from the source. So, to maintain a balance, utilize a data augmentation tool.

3) Validation Set

It is common to make the validation set with a random sample. The method is extremely simple but not efficient. You can produce leaks in your validation set, which means that your image classification model will remember the background details but not the feature itself.

Some data scientists reduce the chances of a leak by using one type of image for the training set and another type of image for the validation set. However, remember to monitor both training and validation sets carefully to avoid overfitting and leaks.

4) Architecture

Once you have decided on your validation set and training set, you need to finalize a computer vision architecture. Fortunately, there are multiple options, such as SqueezeNet, ResNet, Xception, DenseNet, etc.

For example, if you choose ResNet, it is a simple architecture that is perfect for beginner image classification models. This architecture has more than one ResNet block along with identity blocks, which are arranged on top of one another.

In this architecture of image classification, the outcome is joined with the previous convolution outcome. This means that the architecture automatically tries to figure out the difference in the next and previous layer, which leads to improvement in answers.

5) Training Model

To set up your training model for image classification, you can utilize the Fastai library. You just need to write 4-5 code lines to set up a reliable and robust image classification model using CNN.

Follow these steps for image classification training model set up:

i) Establish an image transformation mechanism. This will help your model to prevent overfitting issues we have been discussing for a while now. However, we have already performed augmentations during upsampling. Hence, try and avoid similar augmentations in this step.

ii) After this, set up your data loader, which will contain a training set, test data, and a validation set. This loader will also include data related to batch size, image transformation, and image size.

iii) Now, only finetune your last layer and don’t touch other layers. This will help you get reasonable results before finetuning all the layers. You should keep high learning rates for the last layer of the image classification technique.

iv) Set up or finetune other layers with a little less learning rate because these layers are already good enough at detecting some features.

6) Model Interpretation

When you are making an image classification model, you need to analyze the predictions of the model to ensure efficiency and correctness. You can accomplish this through various methods such as examine top predictions and plot confusion matrix.

However, sometimes, these interpretations can go wrong. Hence, to interpret your model with accuracy, you can use the Grad-CAM technique.

Firstly, utilize the gradients of target layers to highlight significant regions for prediction classes. Then, use backpropagation to extract positive contributions made to the gradient.

7) Improvements

Lastly, improve. There’s always room for improvement in every model. When you start making your image classification model, you don’t know what could go wrong until you train it and test it. Once you have finally made the model, it is time to improve this model by making modifications.

You can change the architecture to better suit your model, or you can modify your validation set for better results. All these improvements should be made before finalizing your model.

Applications of Image Classification

There are multiple applications of image classification models currently put to use in real-world systems. In this portion, we will discuss some of the applications which use various image classification techniques.

1) Visual Inspection System

An automatic visual inspection model is designed to enhance the quality of products in the manufacturing industries.

Here are the varied applications of image classification automatic visual inspection model:

i) Image classification can be used in bulb manufacturing. It is common for bulb filaments to fuse due to faulty geometry. Manual inspection is not sufficient to catch this error with accuracy.

ii) An automatic visual inspection model is also used in industries to identify faulty components. Since these faulty components of electronic systems radiate more thermal power, industries detect them using the infrared images.

iii) Image classification techniques are also used in detecting errors on the surface of rolled metal.

2) Remote Scene Interpretation

Various image classification techniques can be implemented to sense scenes of natural resources remotely. For example, understanding the situation and conditions of forest, soil, and other geological resources helps in flood control, resource planning, etc.

3) Biomedical Imaging

It is common for us to get CT scans, ultrasounds, etc. All these imaging devices are based on image classification models.