Feature Extraction in Computer Vision: Master Image Feature Extraction

Question 1

Which of the following feature descriptors is widely used in computer vision for object detection?

Accepted Answer

Histogram of Oriented Gradients (HOG)

Answer

Average Pooling

Answer

Autoencoders

Answer

K-means Clustering

Question 2

What is the primary objective of feature extraction in computer vision?

Accepted Answer

To extract the essential characteristics of an image while minimizing dimensionality.

Answer

To improve the visual appeal of images.

Answer

To create new features that are not present in the original image.

Answer

To store images efficiently.

Question 3

Which technique is primarily used to capture shape information from an image?

Accepted Answer

Boundary Descriptors

Answer

Gabor Filters

Answer

Principal Component Analysis (PCA)

Answer

Fourier Transform

Question 4

Which deep learning architecture is commonly employed for feature extraction tasks?

Accepted Answer

Convolutional Neural Networks (CNNs)

Answer

Recurrent Neural Networks (RNNs)

Answer

Long Short-Term Memory (LSTM)

Answer

Generative Adversarial Networks (GANs)

Question 5

What is the key distinction between unsupervised and supervised feature extraction approaches?

Accepted Answer

Supervised methods require labeled data, while unsupervised methods do not.

Answer

Unsupervised methods require labeled data, while supervised methods do not.

Answer

Unsupervised methods yield higher-level features, while supervised methods produce lower-level features.

Answer

Supervised methods entail higher computational costs than unsupervised methods.

Question 6

Which of the following is NOT a common feature extraction technique in image processing?

Accepted Answer

Histogram adjustment

Answer

Edge detection

Answer

Principal component analysis

Answer

Fourier transform

Question 7

What is the primary purpose of applying a Gaussian filter in image processing?

Accepted Answer

To suppress noise

Answer

To enhance edges

Answer

To increase contrast

Answer

To sharpen the image

Question 8

Which of the following is a key characteristic of an effective feature extractor?

Accepted Answer

Invariance to transformations

Answer

High dimensionality

Answer

Low computational cost

Answer

Nonlinearity

Question 9

What is the fundamental goal of principal component analysis (PCA)?

Accepted Answer

To reduce dataset dimensionality

Answer

To identify the most significant features in a dataset

Answer

To group data into clusters

Answer

To visualize data in a lower-dimensional space

Question 10

Which of the following is NOT an advantage of using feature extraction in machine learning?

Accepted Answer

Increased data storage requirements

Answer

Enhanced accuracy

Answer

Reduced computational cost

Answer

Increased interpretability

Question 11

How do local and global feature extraction techniques differ?

Accepted Answer

Local techniques analyze small image regions, while global techniques process the entire image.

Answer

Global techniques analyze small image regions, while local techniques process the entire image.

Answer

Local techniques are more computationally expensive than global techniques.

Answer

Global techniques are more robust to transformations than local techniques.

Question 12

Which of the following is NOT a type of feature extraction technique?

Accepted Answer

Support Vector Machines (SVM)

Answer

Principal Component Analysis (PCA)

Answer

Linear Discriminant Analysis (LDA)

Answer

Gaussian Mixture Model (GMM)

Question 13

Which of the following is a drawback of using feature extraction techniques in image classification?

Accepted Answer

Reduced interpretability

Answer

Lower computational cost

Answer

Enhanced accuracy

Question 14

Which of the following is NOT a popular feature descriptor used in image processing?

Accepted Answer

Mean intensity

Answer

Scale-Invariant Feature Transform (SIFT)

Answer

Local Binary Patterns (LBP)

Answer

Harris corner detector

Question 15

What is the primary objective of feature extraction in image processing?

Accepted Answer

To extract meaningful and representative characteristics from images for further analysis or processing.

Answer

To remove image noise and artifacts

Answer

To improve image resolution and clarity

Question 16

Which of the following is NOT a type of feature extraction technique commonly used in computer vision?

Accepted Answer

Clustering

Answer

Principal Component Analysis (PCA)

Answer

Linear Discriminant Analysis (LDA)

Question 17

Explain the fundamental difference between global and local feature extraction methods.

Accepted Answer

Global features represent the entire image, capturing overall characteristics, while local features focus on specific regions, capturing localized patterns and details.

Answer

Global features are robust to noise but sensitive to illumination changes.

Answer

Local features are more efficient but less expressive.

Question 18

Describe the advantages of using feature extraction in image classification tasks.

Accepted Answer

Reduces computational complexity, improves generalization ability, and enhances classification accuracy.

Answer

Eliminates image noise, improves image sharpness, and reduces storage space.

Answer

Enhances image contrast, removes occlusions, and improves image quality.

Question 19

Which feature extraction technique is commonly employed to handle images with poor contrast or uneven illumination?

Accepted Answer

Contrast Limited Adaptive Histogram Equalization (CLAHE)

Answer

Median filter

Answer

Unsharp mask

Question 20

Explain the significance of feature selection in the context of feature extraction.

Accepted Answer

To identify the most relevant and informative features, while eliminating redundant and irrelevant ones, optimizing the feature set.

Answer

To reduce the computational cost of feature extraction.

Answer

To improve the accuracy of the feature extraction algorithm.

Question 21

Which of the following feature extraction techniques is commonly used for image processing?

Accepted Answer

Principal Component Analysis (PCA)

Answer

Random Forest

Answer

Logistic Regression

Question 22

In feature extraction, what is the primary purpose of dimensionality reduction?

Accepted Answer

To reduce the number of features while preserving crucial information.

Answer

To increase model complexity.

Answer

To eliminate irrelevant features only.

Question 23

In a Convolutional Neural Network (CNN), what is the role of the feature extractor?

Accepted Answer

Extracting high-level features from input data.

Answer

Reducing the dimensionality of the input data.

Answer

Classifying images directly.

Question 24

What is an advantage of handcrafted features over learned features?

Accepted Answer

Handcrafted features are typically more interpretable.

Answer

Handcrafted features are more robust to noise.

Answer

Handcrafted features are faster to extract.

Answer

Handcrafted features are always more accurate.

Question 25

Which of the following is a common preprocessing step applied prior to feature extraction?

Accepted Answer

Image resizing

Answer

Classifier evaluation

Answer

Model training

Answer

Feature selection

Question 26

In feature extraction, what is the primary goal of feature normalization?

Accepted Answer

Ensuring that all features are on the same scale.

Answer

Enhancing feature discriminability

Answer

Improving model interpretability

Answer

Reducing the number of features

Question 27

What is a potential drawback of utilizing deep learning for feature extraction?

Accepted Answer

Overfitting may occur if the model is overly complex.

Answer

Deep learning models are always computationally expensive.

Answer

Deep learning models are not appropriate for small datasets.

Question 28

Which of the following is NOT a common feature extraction technique?

Accepted Answer

Smoothing

Answer

Edge detection

Answer

Color histogram analysis

Answer

Principal Component Analysis (PCA)

Question 29

What is the primary purpose of edge detection in feature extraction?

Accepted Answer

To identify boundaries and shapes within images

Answer

To enhance image contrast

Answer

To extract color information

Answer

To reduce noise and artifacts

Question 30

Which of the following is an advantage of using histograms for image feature extraction?

Accepted Answer

Robustness to variations in noise and illumination

Answer

Requirement for a large labeled dataset

Answer

Sensitivity to image size and rotation

Answer

High computational complexity

Question 31

What is the main objective of Principal Component Analysis (PCA) when applied to feature extraction?

Accepted Answer

To reduce dimensionality while preserving maximum variance

Answer

To increase image resolution

Answer

To remove redundant features

Answer

To enhance image contrast

Question 32

How does feature extraction relate to image classification?

Accepted Answer

Extracted features serve as input to classification algorithms

Answer

Feature extraction is a specialized form of image classification

Answer

Image classification must be performed before feature extraction

Question 33

What is a potential drawback of feature extraction?

Accepted Answer

Loss of information during the extraction process

Answer

Enhanced image interpretability

Answer

Reduced computational complexity

Answer

Improved image quality

Question 34

In the context of image segmentation, what role does feature extraction play?

Accepted Answer

Identifying distinct regions of interest

Answer

Detecting objects within the image

Answer

Removing noise and artifacts

Answer

Enhancing image contrast

Question 35

How does feature extraction contribute to the effectiveness of image recognition systems?

Accepted Answer

Provides meaningful representations for classification and retrieval tasks

Answer

Improves the efficiency of image compression

Answer

Reduces the storage requirements of images

Question 36

Which of the following is a widely applied technique for feature extraction in computer vision?

Accepted Answer

Principal Component Analysis (PCA)

Answer

Logistic Regression

Answer

Support Vector Machine

Answer

Naive Bayes

Question 37

What is the primary purpose of feature scaling in the context of feature extraction?

Accepted Answer

To normalize the range of feature values, ensuring compatibility and improving the performance of machine learning algorithms.

Answer

To eliminate outliers that could bias the learning process.

Answer

To reduce the computational cost of feature extraction.

Question 38

Which of the following features is commonly extracted from images for object recognition tasks?

Accepted Answer

Edge detection features

Answer

GPS coordinates

Answer

File size

Answer

User comments

Question 39

What is the main objective of dimensionality reduction in the context of feature extraction?

Accepted Answer

To reduce the number of features while preserving the essential information for analysis, enhancing model interpretability and efficiency.

Answer

To increase the number of features for improved data representation.

Answer

To remove redundant or irrelevant features.

Question 40

Which of the following methods is commonly employed for feature extraction from time series data?

Accepted Answer

Fourier Transform

Answer

K-Means Clustering

Answer

Hidden Markov Model

Answer

Decision Tree

Question 41

What is the significance of local features in image classification?

Accepted Answer

They capture distinctive characteristics within a localized region of an image, providing valuable information for accurate classification.

Answer

They represent global properties of the entire image, making them suitable for scene understanding.

Question 42

When selecting features for a machine learning model, which of the following factors should be considered?

Accepted Answer

Relevance to the prediction task, ability to discriminate between different classes, and computational cost.

Answer

Ease of feature extraction without considering their value.

Answer

Number of features, regardless of their relevance.

Question 43

What is the purpose of using bag-of-visual-words (BoVW) for feature extraction in image classification?

Accepted Answer

To represent an image as a histogram of visual word occurrences, allowing for efficient image comparison and retrieval.

Answer

To extract individual pixels and their intensity values for classification.

Answer

To generate a 3D model of the image for feature extraction.

Question 44

How does feature extraction contribute to the performance of a machine learning model?

Accepted Answer

By providing more relevant and discriminative data, feature extraction helps simplify the learning task and improves the model's accuracy and efficiency.

Answer

It increases the number of features, making the model more complex and prone to overfitting.

Answer

It removes noise from the data, making the model more robust.

Question 45

Which of the following is NOT a traditional hand-crafted feature descriptor used in feature extraction for image processing?

Accepted Answer

Convolutional Neural Network (CNN)

Answer

Scale-Invariant Feature Transform (SIFT)

Answer

Histogram of Oriented Gradients (HOG)

Answer

Speeded-Up Robust Features (SURF)

Question 46

What is the primary goal of feature extraction in image processing?

Accepted Answer

To reduce the complexity of image data by extracting the most informative features for a specific task.

Answer

To identify the dominant colors in an image.

Answer

To enhance the resolution of images.

Question 47

What is a key advantage of using hand-crafted feature extractors compared to deep learning methods?

Accepted Answer

Hand-crafted features can be more interpretable and easier to understand, allowing for better control and debugging.

Answer

Hand-crafted features are more robust to variations in image data.

Answer

Hand-crafted features require less computational resources.

Question 48

Which of the following techniques is NOT commonly used for dimensionality reduction in feature extraction?

Accepted Answer

Convolutional Neural Networks (CNNs)

Answer

t-Distributed Stochastic Neighbor Embedding (t-SNE)

Answer

Linear Discriminant Analysis (LDA)

Answer

Principal Component Analysis (PCA)

Question 49

How does a Histogram of Oriented Gradients (HOG) feature descriptor represent an image?

Accepted Answer

By calculating the distribution of edge orientations and their magnitudes within local regions of the image.

Answer

By analyzing the dominant colors present in the image.

Answer

By representing the image as a set of pixel intensities.

Question 50

What is a significant challenge associated with using Scale-Invariant Feature Transform (SIFT) for feature extraction?

Accepted Answer

It can be computationally expensive, making it less suitable for real-time applications.

Answer

It is sensitive to noise in the image.

Answer

It is not robust to changes in illumination.

Question 51

In the context of image classification, what is the primary advantage of using feature extraction?

Accepted Answer

It simplifies the classification problem by focusing on the most relevant information, leading to more efficient and accurate models.

Answer

It allows for the use of simpler classification models.

Answer

It improves the resolution of images, leading to better classification accuracy.

Question 52

What is the main difference between feature extraction and feature selection?

Accepted Answer

Feature extraction transforms the original data into a new representation, while feature selection chooses a subset of the original features without changing the data itself.

Answer

Feature extraction is used for image processing, while feature selection is used for text data.