Unlock Image Classification: A Practical Guide Without Neural Networks

Deep learning gets all the hype, but you can achieve impressive image classification results without complex neural networks! This guide explores traditional machine learning methods, offering a clear path to build your own image classification pipeline using Python.

Adrien Payong and Shaoni Mukherjee

Why Use Traditional Methods for Image Classification?

Thinking about tackling image recognition without relying on deep learning's black box? You might find classic machine learning is the perfect answer.

• Learnable: Provides a foundational understanding of image classification principles.
• Lightweight Ideal for resource-constrained environments (embedded systems, etc.)
• Explainable: Offers more transparent decision-making compared to deep learning.

Prerequisites: Essential Skills for Image Classification

Before diving in, ensure you're familiar with these concepts:

• Machine Learning Basics: Supervised learning, model training, and metrics (accuracy, F1-score).
• Image Representation: Pixel grids, grayscale, and RGB formats.
• Python Proficiency: Libraries like NumPy, scikit-learn, and OpenCV.
• Feature Extraction: Understanding edges, shapes, and texture analysis.
• Classical Algorithms: Familiarity with SVM, KNN, and Decision Trees.

Supervised Classification: Setting Up Your Image Problem

You'll need these key ingredients for your image classification project.

• Labeled Data: Organize your image dataset, ensuring each image has a correct assigned label.
• Feature Engineering: Convert images into a numeric format for models to process.
• Learning Algorithm: Use algorithms like SVM, Decision Trees, or KNN to map features to labels.
• Comprehensive Evaluation: Gauge model performance using appropriate metrics(accuracy, precision, recall/F1-Score).

Feature Engineering: The Heart of Traditional Image Classification

Feature engineering is crucial for image classification without neural networks. Manually extracted numerical features drive the analysis. Here's a breakdown of common techniques:

Color Features: Painting a Picture with Colors

Describing image colors is a fundamental step.

• Color Histograms: Visualize color distribution in images, showing pixel intensity frequency.
• Color Moments: Utilize statistical summaries (mean, variance) of color channels.

Texture Features: Unveiling Surface Details

Extracting texture helps differentiate images based on surface appearance.

• Gray Level Co-occurrence Matrix (GLCM): Examine spatial relationships between pixel pairs.
• Local Binary Patterns (LBP): Encode texture by comparing pixels to their neighbors.
• Gabor Filters: Extract frequency and orientation information, replicating human vision.

Shape Features: Defining the Geometry

Shape matters, offering a robust way to classify objects.

• Contours: Outline object boundaries in images.
• Moments: Statistically capture shape distribution within images.
• Shape Descriptors: Quantify geometry (circularity, aspect ratio)

Edge Detection: Highlighting Boundaries

Edge information is critical for object recognition.

• Canny Edge Detector: Achieve clean edge detection, minimizing noise and thinning lines.
• Sobel Operator: Highlights horizontal and vertical edges using gradient-based methods.

Keypoint Features: Identifying Distinctive Points

Locating unique key points improves accuracy, scale and rotation invariance.

• SIFT (Scale-Invariant Feature Transform): Detect robust key points, handling scale and rotation.
• SURF (Speeded-Up Robust Features): Focuses on speed to match keypoints in real-time applications.
• KAZE Features: Locate key points, respecting natural image boundaries utilizing nonlinear scales.

Diving Deeper: Texture Feature Extraction Techniques

Let’s explore a few essential texture feature extraction methods:

Histogram of Oriented Gradients (HOG): Capturing Shapes through Gradients

HOG captures local object shape by analyzing gradient directions.

1. Divide the image into small connected cells.
2. Calculate histograms of gradient directions for each cell block.
3. Normalize histograms across cell blocks.
4. Combine histograms to form a feature descriptor.

HOG performs especially well for rigid object detection.

Local Binary Patterns (LBP): Describing Local Textures

LBP captures local texture by comparing each pixel to its neighbors.

1. Select a pixel as the center of a 3x3 grid
2. Compare surrounding pixels to the center. Mark "1" if greater or equal, else "0."
3. Create an 8-digit binary number around the center pixel, creating texture signatures based on neighbor intensity.
4. Multiply binary numbers by respective weights (powers of 2).
5. Sum weighted values creating your LBP value.

LBP is computationally efficient and highly effective for texture classification.

Scale-Invariant Feature Transform (SIFT): Handling Scale and Rotation

SIFT creates descriptors invariant to scale, rotation, and illumination.

1. Locate potential interest points using the difference-of-Gaussian function.
2. Localize key points, removing low-contrast and edge responses.
3. Assign orientation to each key point based on local gradients.
4. Generate descriptors that capture gradient information.

SIFT is a robust method, though computationally intensive.

Traditional Machine Learning Algorithms for Image Classification

With features in hand, it’s time for machine learning!

Support Vector Machines (SVM): Finding the Optimal Separator

SVMs find the hyperplane that maximizes the margin between classes. Effective with feature extraction.

Pros of SVM:

• Works well in high-dimensional spaces.
• Handles non-linear boundaries with kernel functions.
• Resists overfitting with parameter tuning (C and kernel settings).

Cons of SVM:

• Computationally heavy with large datasets and complex kernels.
• Parameter tuning can be challenging and time-consuming.

K-Nearest Neighbors (KNN): Learning from Neighbors

KNN classifies images based on the majority class among its k-nearest neighbors.

1. Calculate the distance between the test image and all training images.
2. Select the K closest images.
3. Assign the class that appears most frequently!

Pros of KNN:

• Simple to implement the classification task.
• Requires no training phase.
• Works well with discriminative feature representation.

Cons of KNN:

• Slow prediction speed for large datasets.
• Requires significant memory.
• Performance heavily relies on the the correct value for K and distance metric..

Decision Trees: Recursive Partitioning for Decisions

Decision trees recursively partition the feature space based on feature splits.

1. Select the feature that best splits data.
2. Create child nodes based on the split.
3. Repeat recursively until stopping criteria are met.

Pros of Decision Trees:

• Highly interpretable results through tree visualization.
• Fast training and prediction with moderate datasets.
• Handles numerical and categorical features well.

Cons of Decision Trees:

• Prone to overfitting.
• Single trees may not achieve the best accuracy.