Feature extraction approach in Computer Vision - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Understanding Feature Extraction in Image Classification

Which of the following best describes the purpose of feature extraction in image classification?

A To reduce the image size by cropping and resizing before classification

B To randomly shuffle image pixels to improve model robustness

C To increase the number of pixels in the image for better resolution

D To transform raw image data into a set of meaningful values that represent important characteristics

💡 Hint

Think about how we simplify complex images into useful information for the model.

Feature extraction transforms raw images into meaningful values that capture important patterns, making it easier for models to learn.

❓ Predict Output

intermediate

Output of Feature Extraction Using SIFT

What is the output type of the following Python code using OpenCV's SIFT feature extractor?

Computer Vision

import cv2
img = cv2.imread('image.jpg', cv2.IMREAD_GRAYSCALE)
sift = cv2.SIFT_create()
keypoints, descriptors = sift.detectAndCompute(img, None)
print(type(descriptors))

A <class 'numpy.ndarray'>

B <class 'list'>

C <class 'NoneType'>

D <class 'dict'>

💡 Hint

Descriptors are numerical arrays representing features.

The SIFT extractor returns descriptors as a numpy array, where each row corresponds to a feature vector.

❓ Model Choice

advanced

Choosing a Model for Feature Extraction

You want to extract features from images to use in a simple classifier. Which model is best suited for automatic feature extraction without manual design?

A A decision tree classifier

B A linear regression model

C A pretrained convolutional neural network like ResNet

D A k-nearest neighbors algorithm

💡 Hint

Consider models that learn hierarchical image features automatically.

Pretrained CNNs like ResNet automatically learn rich image features useful for many tasks, unlike simpler models that need manual features.

❓ Hyperparameter

advanced

Effect of Number of Features in Feature Extraction

When using a feature extractor like ORB, increasing the number of features parameter will most likely:

A Decrease the computation time and reduce feature quality

B Increase the number of detected keypoints and increase computation time

C Have no effect on the number of features detected

D Cause the extractor to ignore edges and focus on colors

💡 Hint

Think about what happens when you ask for more features from the extractor.

Increasing the number of features lets the extractor find more keypoints but requires more processing time.

❓ Metrics

expert

Evaluating Feature Extraction Quality

You extracted features from images and trained a classifier. Which metric best helps evaluate if the features separate classes well?

A Silhouette score measuring cluster separation of features

B Mean squared error between features

C Number of features extracted per image

D Accuracy of the classifier on a test set

💡 Hint

Think about a metric that measures how well features group similar samples apart from others.

Silhouette score measures how well features cluster by class, indicating good feature separation.

Practice

1. What is the main purpose of feature extraction in computer vision?

easy

A. To increase the size of image files

B. To change image colors randomly

C. To convert images into numbers that describe important parts

D. To delete parts of the image

Solution

1. Step 1: Understand feature extraction goal

   Feature extraction transforms images into numerical data representing key details.

2. Step 2: Compare options to this goal

   Only To convert images into numbers that describe important parts describes this process correctly; others describe unrelated actions.

3. Final Answer:

   To convert images into numbers that describe important parts -> Option C

4. Quick Check:

   Feature extraction = convert images to numbers [OK]

Hint: Feature extraction means turning images into numbers [OK]

Common Mistakes:

• Thinking feature extraction changes image colors
• Confusing feature extraction with image resizing
• Believing it deletes image parts

2. Which of the following is a correct way to describe SIFT in feature extraction?

easy

A. A way to convert images to grayscale

B. A method that detects and describes local features in images

C. A technique to increase image resolution

D. A method to compress image files

Solution

1. Step 1: Recall what SIFT does

   SIFT finds and describes important local features in images for matching and recognition.

2. Step 2: Match options to SIFT's function

   Only A method that detects and describes local features in images correctly describes SIFT; others describe unrelated image processes.

3. Final Answer:

   A method that detects and describes local features in images -> Option B

4. Quick Check:

   SIFT = local feature detection [OK]

Hint: SIFT finds key points and describes them [OK]

Common Mistakes:

• Confusing SIFT with image resizing
• Thinking SIFT changes image colors
• Believing SIFT compresses images

3. Given the following Python code using OpenCV, what will be the shape of the feature vector extracted by SIFT for an image with 500 keypoints?

import cv2
img = cv2.imread('image.jpg', cv2.IMREAD_GRAYSCALE)
sift = cv2.SIFT_create()
keypoints, descriptors = sift.detectAndCompute(img, None)
print(descriptors.shape)

medium

A. (null, 128)

B. (128, 500)

C. (500, 64)

D. (500, 128)

Solution

1. Step 1: Understand SIFT descriptor shape

   SIFT descriptors have 128 features per keypoint, so shape is (number_of_keypoints, 128).

2. Step 2: Apply to given keypoints

   With 500 keypoints, descriptors shape is (500, 128).

3. Final Answer:

   (500, 128) -> Option D

4. Quick Check:

   SIFT descriptors shape = (keypoints, 128) [OK]

Hint: SIFT descriptors = keypoints x 128 features [OK]

Common Mistakes:

• Swapping dimensions of descriptors
• Assuming 64 features per keypoint
• Thinking descriptors shape depends on image size

4. You wrote this code to extract features using SIFT but get an error:

import cv2
img = cv2.imread('image.jpg')
sift = cv2.SIFT_create()
keypoints, descriptors = sift.detectAndCompute(img, None)
print(len(keypoints))

What is the likely cause of the error?

medium

A. The image is not loaded in grayscale, causing SIFT to fail

B. SIFT_create() is not a valid OpenCV function

C. detectAndCompute requires a mask argument

D. print(len(keypoints)) is incorrect syntax

Solution

1. Step 1: Check image loading method

   The image is loaded in color by default; SIFT expects grayscale images.

2. Step 2: Identify error cause

   Not converting to grayscale can cause detectAndCompute to fail or return null.

3. Final Answer:

   The image is not loaded in grayscale, causing SIFT to fail -> Option A

4. Quick Check:

   Load image grayscale for SIFT [OK]

Hint: Always load images in grayscale for SIFT [OK]

Common Mistakes:

• Thinking SIFT_create() is invalid
• Believing mask argument is mandatory
• Assuming print syntax is wrong

5. You want to extract features from images for a complex object recognition task. Which approach is best to capture detailed and high-level features?

hard

A. Use a deep learning model like a convolutional neural network (CNN)

B. Use simple edge detection filters only

C. Use random pixel values as features

D. Use image resizing without feature extraction

Solution

1. Step 1: Understand feature needs for complex tasks

   Complex object recognition requires capturing detailed and abstract features.

2. Step 2: Compare methods for feature extraction

   Deep learning models like CNNs learn rich features automatically, outperforming simple filters or random values.

3. Final Answer:

   Use a deep learning model like a convolutional neural network (CNN) -> Option A

4. Quick Check:

   Complex features need CNNs [OK]

Hint: Deep models capture complex features best [OK]

Common Mistakes:

• Relying only on simple filters
• Using random pixels as features
• Skipping feature extraction by resizing only