Data Preparation for Computer Vision Success: Practical Tips & Techniques

With thanks for support from ESF in supporting this research on Feature selection in Computer Vision

Preparing data for computer vision involves more than just collecting images—it requires careful preprocessing, thoughtful feature selection, and understanding the characteristics that make a feature valuable.

Preparing the right data is crucial for building accurate and reliable computer vision models. This article will walk through key steps in preparing data, selecting useful features, and identifying what makes a good feature.

1. Data Collection and Acquisition

The first step in any machine learning project is gathering a high-quality dataset. In computer vision, data generally comes in the form of images or video. To collect a well-balanced and diverse dataset, consider the following:

• Image Quality: Ensure that the images are clear, well-lit, and free from noise.
• Diversity: If building a model for object detection or classification, ensure that the dataset covers a wide variety of scenarios, backgrounds, angles, and lighting conditions.
• Labelling: Labeled data (e.g., bounding boxes for object detection, masks for segmentation) is critical. Accurate labels ensure the model learns the right patterns.

Example: For a facial recognition model, collect images across different facial expressions, ages, and lighting conditions. Label each image with the corresponding identity.

2. Data Preprocessing

• Resizing and Normalization: Computer vision models often require input images of the same size. Resizing ensures consistency across the dataset. Normalization (scaling pixel values between 0-1) makes training faster and helps the model converge better.
• Augmentation: To artificially increase your dataset size and variability, use augmentation techniques such as rotation, flipping, cropping, and color jittering. This helps the model generalize better to unseen data.
• Noise Reduction: Apply filters (e.g., Gaussian blur) to reduce noise and artifacts in images.
• Image Format Conversion: Depending on the model architecture (e.g., grayscale, RGB), convert images to the appropriate format.

Example: Resize all images to 224x224 pixels for a CNN model and apply random rotations to increase dataset diversity.

During data preprocessing, images are not only resized, normalised, and augmented, but they’re also transformed into tensors—multi-dimensional arrays that efficiently store the image data and allow for operations required in computer vision tasks. Each image becomes a 3D tensor with dimensions for height, width, and color channels (e.g., 224x224x3 for RGB images). Tensors allow batch processing and enable the deep learning model to perform fast, efficient computations in subsequent stages.

TensorFlow is an open-source machine learning library developed by Google, widely used for deep learning and neural network applications. It provides tools to build, train, and deploy machine learning models at scale.

Key Features:

1. Flexibility: TensorFlow allows easy model building and supports both high-level APIs like Keras and low-level APIs for custom operations.
2. Computation Graphs: It uses computational graphs where nodes represent operations and edges represent data (tensors) flowing between them.
3. Deployment: It’s optimized for production use, allowing models to run on various platforms like cloud, mobile, and edge devices.

Example: Simple TensorFlow Neural Network

import tensorflow as tf
from tensorflow.keras import layers, models

# Build a simple Sequential model
model = models.Sequential([
    layers.Dense(64, activation='relu', input_shape=(784,)),  # Input layer for a flattened 28x28 image
    layers.Dense(64, activation='relu'),                      # Hidden layer
    layers.Dense(10, activation='softmax')                    # Output layer for 10 classes
])

# Compile the model
model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

# Train the model on the dataset (e.g., MNIST)
# Assuming train_images and train_labels are preprocessed
model.fit(train_images, train_labels, epochs=5)
        

Tensors are integral to each step in computer vision, from representing raw image data to handling complex calculations in convolutional layers, pooling, and ultimately classifying the processed data. Their structure allows for efficient manipulation, making them essential to the speed and accuracy of computer vision models.

In computer vision, tensors are used throughout the entire pipeline, especially when working with deep learning frameworks like TensorFlow and PyTorch, which are designed around tensor operations. Here's where tensors are most commonly used in the process:

1. Input Data Representation: Images are converted to tensors at the very beginning. For instance, an image with height, width, and color channels (like RGB) becomes a 3-dimensional tensor (e.g., shape (height, width, channels)). This format enables easy manipulation, batch processing, and efficient memory handling.
2. Convolutional Layer Operations: During convolution, filters (also represented as tensors) slide over the input image tensor. Each convolution operation multiplies and sums these values, producing a new output tensor that highlights features such as edges, textures, or patterns in the image.
3. Pooling Layers: Tensors are used to represent image patches in pooling layers, which reduce the dimensionality by downsampling while retaining key features. This step uses tensor operations to create condensed versions of feature maps, making computations faster and reducing data complexity.
4. Fully Connected Layers (Classification): After convolution and pooling layers, the tensor (now a flattened, 1-dimensional array) feeds into fully connected layers, where the model maps learned features to specific classes or predictions, such as identifying objects or recognizing faces.
5. Batch Processing for Model Training: Tensors facilitate the processing of multiple images (batches) at once, making the training process faster and more efficient. For example, a batch of 32 images is represented as a 4-dimensional tensor with dimensions (batch_size, height, width, channels).

3. Feature Selection in Computer Vision: with Practical Examples

In any computer vision project, the way you select and define features is crucial to the success of your model. Whether you are using traditional methods or deep learning, feature extraction helps the model learn from the patterns within the image data. Below, we'll look at practical steps for feature selection in a data gathering exercise, with examples that guide this process.

a. Manual Feature Selection

Manual feature selection can be useful in cases where simpler models or low computational resources are needed. Let's assume you are tasked with building a model to identify types of leaves from images for an environmental research project.

Here are some manual feature extraction techniques that you could use:

1. Edge Detection (e.g., Sobel or Canny filters): Edge detection can help capture the shape and outline of the leaves, which are critical for species identification.
2. Colour Histograms: This method helps quantify the colour distribution of images by creating a histogram of pixel intensities for different color channels (RGB). Color is often an important distinguishing feature.
3. Texture Analysis: Texture can be a significant feature in recognizing certain patterns, such as roughness or smoothness.

b. Automatic Feature Selection (Deep Learning Methods)

In many modern computer vision tasks, deep learning (especially Convolutional Neural Networks, or CNNs) is preferred because it allows for automatic feature extraction. The model will learn and select the most relevant features directly from the image data during training, without manual intervention.

Let’s assume you’re developing a model to classify wildlife from camera trap images. Here’s how automatic feature selection works in this scenario:

1. Low-level Features (Edges and Corners): In the early layers of a CNN, the model will automatically extract low-level features such as edges, lines, and corners. These are useful for distinguishing between different animals based on their shape.
2. Mid-level Features (Textures and Patterns): As the layers go deeper, the CNN begins to detect more complex patterns, such as textures or repeated shapes in the images.
3. High-level Features (Objects and Parts of Objects): The deepest layers of the CNN extract high-level features, such as object parts or entire objects. These features are essential for object classification.

Here's a simple visual representation of how a Convolutional Neural Network (CNN) works, breaking down the key steps from input to output.

Python is the most commonly used language for building CNN models, and libraries like TensorFlow or PyTorch are often employed. Here's a sample code to create a basic CNN using Python and TensorFlow:

import tensorflow as tf
from tensorflow.keras import datasets, layers, models

# Load and prepare the dataset
(train_images, train_labels), (test_images, test_labels) = datasets.cifar10.load_data()
train_images, test_images = train_images / 255.0, test_images / 255.0

# Build the CNN model
model = models.Sequential([
    layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)),
    layers.MaxPooling2D((2, 2)),
    layers.Conv2D(64, (3, 3), activation='relu'),
    layers.MaxPooling2D((2, 2)),
    layers.Conv2D(64, (3, 3), activation='relu'),
    layers.Flatten(),
    layers.Dense(64, activation='relu'),
    layers.Dense(10)
])

# Compile the model
model.compile(optimizer='adam',
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
              metrics=['accuracy'])

# Train the model
model.fit(train_images, train_labels, epochs=10, validation_data=(test_images, test_labels))
        

This code demonstrates a simple CNN architecture, which can be modified based on specific datasets and tasks.

4. What Makes a Good Feature?

"Good" features are those that contribute to the model’s ability to make accurate predictions. Good features have several key properties:

a. Distinctiveness

A good feature must be distinct enough to allow the model to differentiate between different objects or classes.

Example: In an exercise where you're classifying traffic signs, features that capture the distinct shapes of different signs (e.g., circular for speed limits, triangular for warnings) are highly effective. Shape-based features, such as edge detection, can be extracted to help the model learn the differences between these classes.

Outcome: Features capturing geometric shapes ensure the model can distinguish between traffic signs with high accuracy.

b. Invariance

Invariance refers to the feature’s ability to remain useful even when the image undergoes transformations such as rotation, scaling, or changes in lighting.

Example: In a facial recognition system, facial features like the distance between the eyes, the shape of the nose, or the position of the mouth must be invariant to changes in pose or lighting conditions. By using features that are invariant to such transformations, the model can recognize faces from different angles or in different lighting.

Outcome: The model generalizes better across various conditions because the selected features are robust to transformations.

c. Minimal Redundancy

A good feature should provide unique information and should not overlap with other features. Redundant features often introduce noise and do not add value to the model.

Example: When classifying handwritten digits from the MNIST dataset, features based on the overall shape (e.g., the roundness of '0' vs. the sharp angles of '7') are important. If you were to extract features based on both shape and the pixel intensity at the center of each image, there may be redundancy between these features. Removing redundant features ensures that the model only focuses on the most important characteristics of the digits.

Outcome: Minimising redundancy leads to a more efficient model, reducing overfitting and improving prediction accuracy.

d. Relevance

Good features must be relevant to the specific problem you are solving. Irrelevant features will reduce model performance, as they do not contribute meaningfully to the prediction task.

Example: In medical imaging for cancer detection, edge features are relevant because tumors often have irregular boundaries. A feature that captures the irregularity or sharpness of the tumor's boundary is highly relevant. Conversely, pixel color may not be as useful if all the scans are in grayscale. By selecting features that focus on the relevant aspects of the image, such as shape and texture, you ensure the model is learning from the right data.

Outcome: Relevant features improve the model’s ability to detect cancerous regions accurately.

5. Feature Engineering and Calculating Variables

Feature engineering transforms raw data into inputs that the machine learning model can use. In computer vision, this might include:

• Pixel Intensities: For simple models, raw pixel values or their averages across regions can be used.
• Principal Component Analysis (PCA): To reduce the dimensionality of images, PCA can be applied, retaining only the most important features.
• Feature Maps: After convolution operations in a CNN, the resultant feature maps contain vital information about patterns within the image. These maps serve as variables for subsequent layers in the model.
• Region of Interest (ROI) Extraction: Instead of feeding the entire image, you can extract key areas where the object of interest resides.

Example: Use PCA to reduce the feature space of a large image dataset, focusing on the most relevant components for a specific classification task, such as identifying disease in chest X-rays.

6. Data Split and Model Evaluation

• Train-Test Split: Ensure your dataset is split into training, validation, and testing sets to avoid overfitting and to evaluate model performance on unseen data.
• Cross-Validation: To ensure robustness, use k-fold cross-validation, where the dataset is split into multiple folds, and the model is trained and validated across all folds.
• Metrics: Depending on the task (e.g., classification, detection), use metrics like accuracy, precision-recall, F1-score, or Intersection over Union (IoU).

Example: In an object detection task, calculate IoU to measure how well the predicted bounding box overlaps with the ground truth bounding box.

7. Producing a Good Model

A good computer vision model depends on the combination of high-quality data, feature selection, and model architecture. Factors that produce a good model include:

• Data Diversity: Ensuring varied and well-labeled datasets.
• Feature Selection: Relying on automatically learned features through deep learning or manually extracting relevant features.
• Avoiding Overfitting: Use regularization techniques such as dropout, augmentation, and proper splitting of data to avoid overfitting to the training data.

Conclusion

By following these steps and best practices, you'll be on your way to building robust, high-performing computer vision models that generalise well to new data.

With thanks for support from ESF in supporting this research on Feature selection in Computer Vision

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