As the digital universe continues to expand, images have become a significant part of the data we generate daily. Leveraging these images for meaningful insights calls for the application of advanced computational methods, particularly in the realm of Artificial Intelligence (AI). This article explains what Image classification is and how it works.

At NumLookup, our Computer Vision team has over 35 years of combined experience in building & testing Image Classification models. Our best-in-class Reverse Image Search platform is a testament to our expertise and years of experience in this field. This article explains how we at NumLookup look at the field of Image Classification and outlines some of the methods and technologies we leverage to power NumLookup's Reverse Image Search.

Image Classification is a fundamental task in Computer Vision that involves assigning a label to an entire image or photograph. Image classification can help computers 'understand' and interpret the world visually, making decisions based on this information. In essence, image classification involves processing an image and outputting a class, or category, the image belongs to. The process entails various steps:

1- Collecting Training Data to Kickstart Image Classification

The first step in image classification is gathering a dataset of images that have already been labeled. For instance, if the task is to differentiate between cats and dogs, the dataset would consist of many images of cats and dogs, each labeled with the correct species.

2- Preprocessing the Data for Image Classification

Images in the dataset are then often preprocessed to improve the model's training efficiency. This preprocessing could include resizing images so they all share the same dimensions, normalizing pixel values, augmenting the data by creating altered copies of the images (e.g., rotating or zooming into the image), and splitting the dataset into training and testing subsets.

3- Choosing an Image Classification Model. What are Image Classification Models?

Next, a machine learning model is chosen for the task. The most common types of models for image classification are Convolutional Neural Networks (CNNs) because they are exceptionally good at capturing spatial hierarchies, i.e., they can understand features at various levels of abstraction. For instance, a CNN can recognize edges in the lower layers and more complex shapes or structures in the higher layers.

Below are the top 5 Image Classification Models

1. LeNet-5: Developed by Yann LeCun in 1998, LeNet-5 is considered one of the earliest convolutional neural networks. It was primarily used for digit recognition, such as reading zip codes, digits in bank checks, etc.
2. AlexNet: This model, proposed by Alex Krizhevsky, used Convolutional Neural Networks (CNNs) and won the 2012 ImageNet Large-Scale Visual Recognition Challenge (ILSVRC), significantly outperforming models based on traditional computer vision techniques. AlexNet has a similar architecture to LeNet but is deeper and bigger and was the first to illustrate the power of CNNs and GPU training.
3. VGGNet: The VGGNet model, which was introduced by the Visual Graphics Group (VGG) at Oxford, is known for its simplicity and uniformity. VGGNet consists of 16 convolutional layers and is very appealing due to its very uniform architecture. Similar to AlexNet, it has only 3x3 convolutions, but lots of filters. It was a runner-up at ILSVRC 2014.
4. GoogLeNet/Inception: This model was a winner in the ILSVRC 2014 competition and was developed by Szegedy et al. from Google. The biggest contribution of this model was the inception module, which drastically reduced the number of parameters in the network (4M, compared to AlexNet with 60M). The inception module is essentially a concatenation of features learned by various filters in the same layer.
5. ResNet (Residual Network): ResNet, developed by Kaiming He et al., introduced "skip connections" or "shortcuts" to allow gradients to flow through the network directly. The ResNet model significantly improved the state of the art on ImageNet, COCO object detection, COCO segmentation, and other databases. It was the winner in the ILSVRC 2015 competition.

4-Training the Image Classification Model

The model is trained using the training dataset. During training, the model tries to learn features that are important for differentiating between the classes. For example, the model might learn that cats have pointed ears and dogs have round ears. This learning process involves forward propagation (passing the images through the network), calculating the loss (how much the prediction deviates from the true label), and backpropagation (updating the model's parameters to minimize this loss).

5-Testing & Evaluation of Image Classification Model

After the model has been trained, it is tested using the testing dataset. This dataset contains images the model has not seen before, making it a good indicator of how well the model has learned to generalize from the training data.

6- Making Determinations/Predictions

Making Predictions: Once the model has been trained and evaluated, it can be used to classify new, unseen images. When a new image is inputted into the model, it outputs the class the image most likely belongs to.