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What is Image Classification
Image Classification is a task of Computer Vision hat involves assigning a single label to tell what is present in an image. It involves receiving the pixels of the image and introducing them to different techniques from mathematical functions to deep learning that have predefined labels as outputs and tell what is in the image assuming one primary subject from the given labels, unlike Object Detection which points out what and where in the image an object is.
Since the process involves pointing out something that is easily visible to the naked eye (or not) from a bunch of numbers, the task becomes tricky because of the abstraction of certain challenges inherent in the images. Like orientation, scale, deformations, occlusions or illumination conditions, to name a few. For more on what is computer vision.
How Image Classification Works: Step by Step
- Defining the task: Building an image classification process involves, as in most processes, defining the task in a precise manner. It's important to answer questions, such as:
- What objects will be labeled? For instance, between bolts and nails, which are geometric objects, is very different from classifying handwritten letters.
- What does the background look like? It may be an homogeneous background, very distinctive from the object or a changing background with different shapes appearing from time to time.
- Where will the classifier be implemented? Will it work on the production line or will it be running on a cloud service?
With the answers in mind, the definition may be: Classify images of mats as either normal or broken, the background will be a cement floor that contrasts with the colour of the mats, and will run on edge at the factory.
- Prepare data: It’s important to keep in mind all the different situations that could happen while performing the task, as defined on the previous step. Having enough examples of harsh backgrounds, rotations, different angles or illumination is momentous to get a robust final solution. At this stage the dataset is split on train, test and validation sets.
- Determine and Set up the approach: The approaches could be divided in two categories: Classical Methods and Deep Learning Methods. As we will discuss in the next section, determining the approach to follow will hinge on the task, the data and the computational capacity available for the task. Once determined, the next step is setting it up.
- Feature extraction: Regardless of the approach, the next step is feature extraction, while in Classical Methods this is handcrafted, the deep learning methods automatically learn which features define the classes while training.
- Evaluation - Optimization: Beyond the classification well performed, it finally should be necessary to see if the solution works on the expected times according to the compute power we have. In order to accomplish the defined task.
Traditional Methods vs. Deep Learning Approaches
Image Classification, like many areas of Computer Vision, can be approached through two distinct paradigms: Traditional Methods and Deep Learning Methods.
The Traditional Methods rely on handcrafted feature extraction techniques combined with classical machine learning methods to classify those features. Despite appearing in an early stage, they’re widely used in certain applications, owing to the lower computational demands, the ability to work with less data and higher interpretability.
Deep Learning Methods by contrast, rely on neural networks that learn the relevant features on their own. The Convolutional Layers are responsible for capturing the relevance of each class while keeping those features scale, shift and distortion invariant [1].
The output of the Convolutional Layer becomes a feature map, a set of 2D matrices with higher values in the regions where certain patterns are present, after that, a pooling layer summarizes that information. In the end a fully connected layer will map those features to each class, assigning a probability and selecting which is the most probable one. The final ingredient, which also gives the method its name, is depth, allowing Deep Neural Networks to capture very complex features, achieving more precise classification under challenging conditions.
Most Popular Algorithms and Architectures in Image Classification
Classical Approach
The Classical Approach has three basic steps: Feature Extraction, Summarization, and Classification.
Feature Extraction
The goal is to find meaningful mathematical representations of the visual content, allowing it to be targeted in most situations. There are various features of different kinds, including Geometrical, Statistical, Texture, and Color, to name a few.
Common feature descriptors are:
Summarization
As the descriptors can produce multiple features, the summarization stage is responsible for converting all the features into a fixed-length vector. Since a vector is a point in the vector space, summarization brings the concept of closeness to similarly-featured images, so images that look alike will produce vectors near each other in vector space.
Those methods should be compact enough to be stored and compared efficiently, discriminative enough to keep similar images close enough, and distinct images far apart, and robust to the descriptor features.
Below are some of the most common vectorization methods used.
Classification
With the vector, we are ready to split the space into dichotomies that retain the classes within them.
Below are the most common classifiers that finish the pipeline for classical methods: Support Vector Machines, k-Nearest Neighbors, and Random Forests.
Deep Learning Approach
Deep learning replaces hand-crafted feature descriptors by learning hierarchical feature representations directly from raw images. In contrast, they need large labeled datasets in training to learn how to predict the classes and are less interpretable than classical approaches. Despite those drawbacks, deep learning is very accurate, making it very useful and a more universal solution than classical methods, tailor-made to the problem they were aiming to solve.
There are at least two pipelines in Deep Learning, the Training Pipeline and the Inferencing Pipeline.
Training Pipeline
Preprocessing
The models usually need a preprocessing stage to be able to apply the model to an image, like resizing, normalization of values to 0-1, to name a few. This assures the model will receive an image it can handle
Training
With the bunch of images preprocessed, the model is trained to learn features automatically. Early layers detect simple structures such as edges or color gradients, while deep layers combine those edges into complex shapes like eyes, textures, or leaves.
Transfer learning and Finetuning
As the models increase in complexity and size, training becomes more computationally expensive, and often, there are not enough labeled images to train them. In addition, models can rely on spurious correlations in training data in rare examples of a class. To overcome that, the models are usually finetuned, which means that the late layers are retrained with a very low learning rate while the former layers are frozen with locked weights.
On the other hand, Transfer Learning leverages a pre-trained model by freezing most layers and changing the last one, called the head, to return the desired labels.
Inferencing Pipeline
During inference, input images are preprocessed with the pipeline used in training to ensure consistency in model outputs. Then the preprocessed image is fed into the model to make the inference that produces the final class prediction.
Deep Learning Architectures used for Image Classification
State of the Art
The State of the Art (SotA) in image classification is currently dominated by Vision Transformers (ViT) and architectures that combine transformers with CNNs or are inspired by them. While ViT research focuses on improving scalability, hybrid approaches (ViT + CNNs) aim to improve efficiency by combining the accuracy of ViTs with the low latency of CNNs.
Side Note: Foundation and Multimodal Vision Models
As AI Research evolves and models increase their capabilities, several architectures have emerged that go beyond image classification. The models go beyond image classification by learning contextual and multimodal representations. These models demonstrated competitive performance on this task in terms of accuracy compared to the top-tier architectures in the field.
Common Challenges in Image Classification Projects
The challenges on Image Classification are transversal to all approaches, but in a different measure. The following paragraphs describe most of them.
Data-related challenges
These challenges are usually about the limitations to produce a high quality dataset, usually one of these.
- Lack of labeled data: Insufficient images for training the model or getting the features.
- Class Imbalance: The lack of labeled images on certain infrequent or difficult-to-capture classes. It may produce biased results, making the model useless if the problem is correctly classified as a rare class.
- Noisy Labels: Labels are difficult to define in certain cases, even for experts. This leads to annotation uncertainty.
- Domain Shift: Mismatch between training data distribution and real-world data distribution. For instance, when the labels don’t generalize, such as classifying crops in different soil types or using different cameras.
Generalization Issues
- Overfitting: Deep learning models are prone to performing well on the training dataset but poorly on unseen test data.
- Underfitting: The model is too simple for the task, so it can’t capture the features that would properly differentiate between labels.
- Shortcut learning: When the solution is not expressive enough to distinguish the classes. The difference with underfitting is that, for example, in a cats vs. dogs task, if not well trained, it may learn that “cats often appear indoors on sofas, and dogs often appear outdoors in grass”.
Evaluation Issues
As accuracy does not imply robustness, performance evaluation may be an iterative and practical process requiring multiple metrics and testing conditions to properly assess the task.
Real World Applications
Image Classification is used in a wide range of applications. In computer vision pipelines, it decides whether to trigger more computationally expensive vision tasks, such as image segmentation and object detection.
In satellite imagery processing pipelines, huge satellite images are split into tiles and processed; Image Classification detects what is on the tile. Let’s say it detects the presence of forests, then an image segmentation pipeline is started to identify the actual part of the image with a forest, while sea-only tiles are not considered. This way is possible to measure the area of forests, identify Antarctic nunataks, city growth, crop diseases, among others.
In medical imaging, the process is similar. As there are large images, it uses Image Classification to detect diseases on an MRI. Then, a CNN classifies the orientation of the MRI Image, and multiple ViTs will classify whether the Image is normal or abnormal [2].
There are also Image Classification techniques used in engine sound classification, which uses the spectrogram of the noise of broken, working, and heavily loaded engines to classify them[3].
Samsung Galaxy Cameras improve moon pictures by recognizing the presence of the moon on a picture to improve the image quality with AI. Here, Image Classification is used as a trigger to apply their moon enhancement algorithms.
Samsung Galaxy Cameras improve moon pictures by recognizing the presence of the moon on a picture to improve the image quality with AI. Here, Image Classification is used as a trigger to apply their moon enhancement algorithms.
Benefits of Implementing Image Classification in Organizations
Image Classification is foundational yet very important tasks in Computer Vision, it’s usage can create measurable value. Needless to say, Image Classification is not for every Organization, but there are many benefits in different production chains, and decision making processes. Let’s point them out.
- Cost Reduction in manual work: As the classification may be way faster and consistent than labor, the automatization with image processing should reduce costs.
- Scale processes: Once the model and equipment to run it is ready, it can be applied as much as needed with 24/7 operation time.
- Improve quality controls: The models, albeit statistical in nature, tend to be more accurate when defining a rule than a group of people, ensuring a more objective, consistent standard.
- Accelerate Decision Making: It accelerates the decision making process because of the speed and ease of availability of the data, giving live statistics on the process step that relies on image classification.
References
[1] Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proceedings of the IEEE, vol. 86, no. 11, pp. 2278–2324, Nov. 1998.
[2] B. Subramanian et al., "AI-Driven MRI Spine Pathology Detection: A Comprehensive Deep Learning Approach for Automated Diagnosis in Diverse Clinical Settings," arXiv:2503.20316, Mar. 2025.
[3] H. Uzel, Y. Özüpak, F. Alpsalaz, E. Aslan, and I. Zaitsev, "Acoustic-based fault diagnosis of electric motors using Mel spectrograms and convolutional neural networks," Sci. Rep., 2025, doi: 10.1038/s41598-0125-33269-z.
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