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What is Image Classification? An Overview
Article n°4 of the “Deep Learning for Computer Vision” series
Image classification is a topic of pattern recognition part of the computer vision domain. It aims to reduce the semantic gap, which is the difference between how a human perceives the contents of an image versus how an image can be represented in the way a computer can understand its content.
In other words, image classification takes as input an image and output a class (e.g. a cat, a dog, …) or a probability of classes that best describes the content of the image. An example of this task and its output is given below.
This task is not as easy because several challenges are faced when processing images in computer vision, e.g. viewpoint variation, scale variation, deformation, occlusions, illumination, background chatter, and interclass variation …
In Deep Learning; the image classification is done in four steps. First, you gather the data and annotate it, split it to train and validation sets, then train the model and finally evaluate it.
More details about each step:
Step 1 — Gather dataset:
- It is usually preferable to insure that you have the same number of images per category (class) otherwise the final trained model will be biased.
Step 2 — Split dataset
- Split the data into two sets: training and validation datasets. Both sets should not overlap. Common split (66% 33% or 75% 25% or 90% 10%)
- The split (train/validation) is one of the parameters (known as “hyperparameter” ) used to tune the trained model.
