Computer Vision with ML: Basics Quiz Quiz

Explore essential concepts in computer vision using machine learning with this quiz, covering image processing, feature extraction, model types, and evaluation techniques. Perfect for those looking to solidify their understanding of foundational computer vision principles and terminologies.

1. Distinguishing Image Classification from Segmentation

   In computer vision, which task involves assigning a single label to an entire image, such as identifying if an image contains a cat or a dog?

   1. Image Classification
   2. Object Detection
   3. Pose Estimation
   4. Semantic Segmentation

   Explanation: Image classification refers to assigning a single label to an entire image, for example, predicting whether the image contains a cat or a dog. Semantic segmentation assigns a label to each pixel, distinguishing between different objects at the pixel level. Object detection involves both classification and localization of objects within an image. Pose estimation focuses on identifying the position of key points, such as joints on a human figure. Therefore, image classification is the correct answer, while others involve more detailed or different image annotations.

2. Feature Extraction Basics

   What is one primary reason for using feature extraction techniques in machine learning-based computer vision tasks, especially when working with high-resolution images?

   1. To reduce computational complexity by transforming raw data into informative representations
   2. To convert color images to grayscale only
   3. To randomly shuffle the order of pixels for regularization
   4. To increase the number of pixels in an image for better details

   Explanation: Feature extraction is used to reduce computational complexity by summarizing large images into more manageable and informative representations, which can improve learning efficiency and accuracy. Simply increasing the number of pixels is unrelated to reducing complexity and might actually make processing harder. Randomly shuffling pixels would destroy spatial relationships needed for vision tasks. Converting images to grayscale may be a preprocessing step but is not feature extraction itself. Thus, reducing complexity through informed representation is the correct purpose.

3. Understanding Convolutional Neural Networks (CNNs)

   Why are convolutional layers particularly suitable for processing visual data in computer vision tasks such as recognizing handwritten digits?

   1. They perform better with audio data than image data
   2. They rely on manual extraction of features only
   3. They efficiently capture spatial hierarchies and local patterns in images
   4. They ignore the spatial arrangement of pixels

   Explanation: Convolutional layers exploit the spatial structure of images, capturing local features like edges or corners and building up complex patterns through multiple layers. They do not rely solely on manual feature extraction; instead, they learn relevant patterns automatically. Ignoring spatial arrangement would defeat the purpose of convolutional operations. CNNs are specially designed for images, not audio, making the other options incorrect.

4. Common Evaluation Metrics in Computer Vision

   When evaluating an object detection model, which metric measures the overlap between the predicted bounding box and the ground-truth box?

   1. Intersection over Union (IoU)
   2. Mean Squared Error (MSE)
   3. Confusion Matrix
   4. Precision-Recall Curve

   Explanation: Intersection over Union (IoU) directly measures the degree of overlap between the predicted and true bounding boxes, providing an important metric for object detection accuracy. Mean Squared Error (MSE) is typically used in regression tasks, not bounding box evaluation. The precision-recall curve summarizes a classifier's tradeoff, but does not specifically quantify bounding box overlap. The confusion matrix is for classification results, not object localization. IoU is thus the most appropriate metric for this use.

5. Dealing with Overfitting in Image Classification

   Which tactic is commonly employed during the training of machine learning models for computer vision to help reduce overfitting, especially when the dataset is limited?

   1. Reducing Training Data
   2. Disabling All Regularization
   3. Increasing Network Depth Indefinitely
   4. Data Augmentation

   Explanation: Data augmentation helps reduce overfitting by artificially increasing the diversity of the training set using techniques like rotation, flipping, or cropping images. Simply increasing the depth of a network can make overfitting worse if not controlled. Disabling regularization removes helpful constraints designed to prevent overfitting. Reducing the amount of training data exacerbates overfitting. Data augmentation is an effective and widely-used solution for this challenge.