Have you ever wondered how search engines can recommend the right images based on your query, or how social media apps can effortlessly identify faces in uploaded photos? One key technology enabling these capabilities is image classification.

As an important technique in computer vision and artificial intelligence, image classification assigns digital images into predefined categories with increasing precision and accuracy. By laying the foundation for visual data analysis, this technology is driving enhanced understanding and better utilization of image information across numerous industry applications.

So, what exactly is image classification? What are its types? How does it work?

In this article, we'll answer these questions from the following content:

1. What is Image Classification

2. Technical Types of Image Classification

3. Image Classification and Deep Learning

4. Applications of Image Classification

5. How to Classify Images with a Multi-level Tag System

6. Frequently Ask Questions

What is Image Classification

Image classification is a fundamental task in computer vision and machine learning. It involves categorizing and labeling images based on specific rules applied to their pixels or vectors. Simply put, it enables computers to recognize and classify the content within images. For instance, given a set of images containing cats, dogs, and rabbits, the task of image classification would be to distinguish the cat images, the dog images, and the rabbit images from one another.

Image classification typically involves the following steps:

• Data Collection: First, gather a large amount of image data. This data can be either labeled or unlabeled.

• Feature Extraction: Extract features from the images that can represent their content. Feature extraction can be carried out in two ways, manual feature extraction and automatic feature extraction.
  

• Classification Decision: Assign the image to the appropriate category based on the extracted features. For instance, if the features of an image indicate the presence of a cat, the image can be classified as a "cat" image.
  

Methods of Image Classification

Here are the four types of image classification: Binary Classification, Multiclass Classification, Multilabel Classification, and Hierarchical Classification. Each type is explained in detail, highlighting their differences, and followed by an example.

Binary Image Classification: In this type, images are categorized into one of two mutually exclusive classes. Imagine a project focused on distinguishing between cats and dogs; the input is an image containing either a cat or a dog, and the output is either "cat" or "dog", but not both.

Multiclass Image Classification: Here, images are classified into one of several mutually exclusive categories, with each image belonging to only one class. Consider a project that needs to identify various animals like cats, dogs, and rabbits. The input is an image of a cat, a dog, or a rabbit, and the output is one of "cat," "dog," or "rabbit".

Multilabel Image Classification: This type allows images to be assigned to multiple classes simultaneously, meaning each image can belong to more than one category. Think of a project that requires identifying all animals present in an image. An image might contain both a cat and a dog, so the input is an image that may include multiple animals, and the output could be "cat" and "dog" at the same time.

Hierarchical Image Classification: Images are categorized into a hierarchy of classes, where categories have parent-child relationships. Picture a project that needs to classify animals hierarchically down to species and subspecies. The input is an image of a specific animal, and the output is a hierarchical classification like "Animal > Mammal > Felidae > Domestic Cat".

Types of Image Classification Algorithms

Supervised Learning

Supervised learning is like teaching a child to recognize different fruits. You show them a picture and tell them it's an apple, then show them another picture and tell them it's a banana. By repeatedly showing pictures and naming the fruits, the child gradually learns how to distinguish between apples and bananas. Supervised learning follows a similar process, where a computer learns from a large set of labeled data to classify new data.

Supervised learning encompasses many algorithms, each with its unique advantages and suitable scenarios. Some common supervised learning algorithms include linear regression, K-nearest neighbors, logistic regression, support vector machines, decision trees, random forests, and neural networks.

Let's briefly review a few of these algorithms.

• K-nearest neighbors (KNN): This algorithm predicts the category of a new data point by calculating the distances to all data points in the training set, finding the K nearest neighbors, and assigning the category based on the majority class of these neighbors.

• Support vector machines (SVM): SVMs work by finding the optimal hyperplane that separates data points of different classes, achieving classification or regression.

• Decision trees: This algorithm classifies data through a series of binary decisions, where each node represents a feature and each branch represents a decision outcome.

Unsupervised Learning

In contrast to supervised learning, which uses labeled images to train models for specific categories, unsupervised learning operates without such labels. This means the model must identify and group similar images on its own, making it particularly useful for tasks such as clustering similar images, anomaly detection, and exploring large datasets for hidden structures.

Common algorithms used in unsupervised image classification include K-means and Gaussian Mixture Models (GMM). K-means clustering divides images into K distinct clusters based on feature similarity, effectively grouping similar images together. Gaussian Mixture Models, on the other hand, assume that images are generated from a mixture of several Gaussian distributions and cluster the images accordingly, allowing for more flexible cluster shapes.

Semi-supervised Learning

Semi-supervised learning is a method that combines both labeled and unlabeled data for training. It leverages a small amount of labeled data to guide the model while extracting useful information from a large amount of unlabeled data, thereby improving the model's accuracy and generalization. This approach is particularly effective when labeled data is scarce or expensive to obtain.

Image Classification And Deep Learning

Convolutional Neural Networks (CNNs) are the core technology in the field of image classification. CNNs extract and classify features from images through convolutional operations, pooling operations, and fully connected layers, significantly improving the accuracy and efficiency of image classification.

Below are six currently popular models:

AlexNet: Proposed in 2012 and achieved groundbreaking results in the ImageNet competition. This model significantly improved image classification accuracy by introducing the ReLU activation function and large-scale data augmentation techniques.

VGGNet: It is known for its simple yet deep architecture. It uses very deep networks (such as VGG16 and VGG19) with small 3x3 convolutional kernels to capture fine details, making it perform exceptionally well in image classification tasks.

ResNet: ResNet addresses the vanishing gradient problem in deep networks by introducing residual connections. These connections enable the training of very deep networks, leading to excellent performance in multiple image classification benchmarks.

Inception (GoogLeNet): Proposed by Szegedy et al. in 2014, the Inception network captures features at different scales within the same layer by using convolutional kernels of various sizes. The Inception module achieves efficient computation and excellent performance, making it stand out in image classification tasks.

EfficientNet: It balances network depth, width, and resolution through a compound scaling method. It achieves higher efficiency and accuracy in multiple image classification benchmarks, which is especially suitable for resource-constrained applications.

YOLO: Although primarily used for object detection, YOLO is based on image classification. It achieves real-time detection by classifying and locating objects simultaneously.

Applications of Image Classification

Medical Imaging Analysis

In the medical field, image classification plays a crucial role in early disease detection and diagnosis. For instance, studies have demonstrated that convolutional neural networks can achieve over 90% accuracy in detecting diabetic retinopathy.

Autonomous Driving

Image classification technology is one of the primary capabilities that enables self-driving cars to perceive their surroundings and make informed decisions. Taking Cruise's autonomous vehicles as an example, they integrate image classification with other techniques to process sensor data and dynamically adapt their driving strategies based on real-time road conditions. The application of this technology allows self-driving cars to detect and respond appropriately to various environmental conditions, such as rainy or snowy weather, thereby ensuring a safer and more efficient driving experience.

Agriculture

Image classification technology has shown the potential to enhance certain agriculture practices, contributing to improved crop monitoring and earlier detection of some plant diseases. Research indicates that utilizing CNNs for identifying crop diseases can achieve an impressive 98% accuracy. One practical example is the PlantVillage app, which assists farmers in diagnosing plant diseases through photos of their crops, thereby enhancing agricultural productivity and preventing large-scale crop losses.

Retail

In the retail sector, the automated product identification process is adopted to improve inventory management, with image classification being one of the techniques employed. Therefore, by enhancing inventory visibility and streamlining service processes, this process aids in improving customer experience. Retailers who implement this technology have reported a 20% reduction in stockouts, highlighting its efficiency.

How to Classify Images with a Multi-level Tag System

While image classification is the simplest task in data annotation, some real-world scenarios are more complex, requiring multi-level and multi-factor data annotation for a single image. Therefore, it is crucial to establish a comprehensive, multi-level, and reusable tag system. The following will explain how to build a well-structured ontology system in the BasicAI Cloud*.

We'll use the classification of hawthorn images as our example, a process commonly employed for detecting agricultural pests and diseases, predicting yields, and similar applications.

Step 1: Data Preparation and Upload

The first step in using BasicAI Cloud* for image classification is to upload the data you need to classify. Users can upload data in various formats via local addresses, URLs, and cloud storage. For instance, if you need to classify hawthorn images, you can upload them to BasicAI Cloud*. The process is straightforward: select the files and click the upload button.

Step 2: Ontology Creation

Next, you'll need to create the ontology in the Ontology Center, adhering to the project's specific annotation guidelines and rules. BasicAI Cloud*'s Ontology Center allows for creating multi-level, multi-attribute, and reusable class and classification ontologies.

🌟 What is class and classification in BasicAI Cloud*?

For our hawthorn image classification example, you'll want to create ontologies for types like environmental conditions, image quality, and hawthorn varieties. Start by clicking the Create icon in the Ontology Center to create a new group for hawthorn image classification (you could name it "Hawthorn Image Classification," for instance).

Within this group, proceed to create the required ontologies for each classification, one by one. To create the environmental conditions ontology with options like sunny, overcast, and night, first enter the ontology name, then click Manage Attributes to set the specific option details.

Once you've completed one classification ontology, you can continue creating the next one within the same group.

After creating all the required classification ontologies, head back to the dataset interface, click the Ontology button, and select Copy from Ontology Center. This will apply the entire hawthorn image classification group, previously set up in the Ontology Center, to your current dataset.

This feature significantly boosts annotation efficiency. For subsequent annotation tasks involving the same labels, you can simply copy the entire group from the Ontology Center, eliminating the need for recreation and saving considerable time and effort.

Step 3: Image Classification

With everything set up, you're now ready to commence image classification. Simply open your dataset and navigate to the Image Classification module.

Step 4: Data Export

The final step involves exporting your annotated data in your preferred format, such as JSON.

Frequently Asked Questions

Image Classification vs. Object Detection vs. Segmentation

Image classification, object detection, and segmentation serve related yet distinct purposes in computer vision applications. Classification assigns the entire image to a predetermined category, identifying the predominant object or scene. Detection, on the other hand, localizes and classifies one or more objects by drawing bounding boxes around them. Segmentation takes this a step further by classifying individual pixels, rather than whole images or bounding boxes, to precisely delineate and outline target objects.

As evident, classification tasks categorize the overall image, while detection locates and bounds objects of interest, and segmentation granularly depicts the contours and shapes of those objects. Each task suits different scenarios - classification for image tagging, detection for identifying and locating all relevant objects, and segmentation for precise delineation of target outlines. Recognizing these distinctions aids in selecting the appropriate computer vision model for a given problem.

Advantages of Deep Learning vs. Traditional Methods for Feature Extraction

Deep learning can automatically learn features from raw data, providing high accuracy and superior performance on complex tasks, especially with large datasets. However, it requires significant computational resources. Traditional image processing methods are often more efficient and interpretable, working well in scenarios with limited data and lower computational requirements, but they may not match the accuracy of deep learning for intricate patterns.

What are the challenges in image classification?

Challenges in image classification include handling variations in lighting, occlusions, and backgrounds, as well as dealing with large and imbalanced datasets. Ensuring high accuracy and generalization to new, unseen data also poses significant difficulties.

* To further enhance data security, we discontinue the Cloud version of our data annotation platform since 31st October 2024. Please contact us for a customized private deployment plan that meets your data annotation goals while prioritizing data security.