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Computer Vision
Image Processing
Feature Detection
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Image Classification
Semantic Segmentation
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Object Recognition in 3D
Gesture and Hand Tracking
Scene Understanding
Image Classification Quiz Questions
1.
Which image classification method is known for its ability to classify images by identifying key visual words or visual features?
A. Histogram equalization
B. Bag of Visual Words (BoVW)
C. Median filtering
D. Sobel operator
view answer:
B. Bag of Visual Words (BoVW)
Explanation:
The Bag of Visual Words (BoVW) method classifies images by identifying key visual words or features.
2.
In image classification, what is the primary advantage of using "convolutional neural networks" (CNNs) over traditional machine learning methods?
A. Noise reduction
B. Real-time performance
C. Ability to automatically learn hierarchical features from data
D. Precise image resizing
view answer:
C. Ability to automatically learn hierarchical features from data
Explanation:
Convolutional Neural Networks (CNNs) can automatically learn hierarchical features from data, improving image classification accuracy.
3.
What is the primary purpose of "transfer learning" in image classification?
A. Reusing pre-trained models on a new, related image classification task
B. Noise reduction
C. Color correction
D. Image resizing
view answer:
A. Reusing pre-trained models on a new, related image classification task
Explanation:
Transfer learning in image classification involves reusing pre-trained models on a new, related image classification task.
4.
Which image classification technique is based on the idea of "max-pooling" to downsample feature maps and reduce spatial dimensions?
A. Histogram equalization
B. Median filtering
C. Max-pooling
D. Sobel operator
view answer:
C. Max-pooling
Explanation:
Max-pooling is used to downsample feature maps and reduce spatial dimensions in image classification.
5.
What is the primary goal of "image normalization" in image classification?
A. Image resizing
B. Noise reduction
C. Reducing variations in image appearance to improve model robustness
D. Color correction
view answer:
C. Reducing variations in image appearance to improve model robustness
Explanation:
Image normalization reduces variations in image appearance to improve model robustness in image classification.
6.
In image classification, what does "fine-tuning" refer to?
A. Noise reduction
B. Refining a pre-trained model on a specific image classification task with a smaller dataset
C. Image resizing
D. Color correction
view answer:
B. Refining a pre-trained model on a specific image classification task with a smaller dataset
Explanation:
Fine-tuning involves refining a pre-trained model on a specific image classification task using a smaller dataset.
7.
Which image classification approach is based on the idea of "bagging" and constructing an ensemble of multiple classifiers?
A. Histogram equalization
B. Bagging with Random Forest
C. Median filtering
D. Sobel operator
view answer:
B. Bagging with Random Forest
Explanation:
Bagging with Random Forest involves constructing an ensemble of multiple classifiers in image classification.
8.
What is the primary challenge in image classification when dealing with complex scenes with multiple objects?
A. Image resizing
B. Color correction
C. Noise reduction
D. Object recognition and localization
view answer:
D. Object recognition and localization
Explanation:
The primary challenge in image classification with complex scenes is object recognition and localization.
9.
Which image classification method is based on the idea of extracting features from regions of an image and using them for classification?
A. Histogram equalization
B. Gabor filter
C. Local Binary Patterns (LBP)
D. Median filtering
view answer:
C. Local Binary Patterns (LBP)
Explanation:
Local Binary Patterns (LBP) extracts features from regions of an image for classification in image classification.
10.
What is the primary purpose of "class imbalance handling" techniques in image classification?
A. Image resizing
B. Color correction
C. Noise reduction
D. Addressing the problem of imbalanced class distribution in the dataset
view answer:
D. Addressing the problem of imbalanced class distribution in the dataset
Explanation:
Class imbalance handling techniques address the problem of imbalanced class distribution in the dataset in image classification.
11.
Which image classification method is known for its ability to classify images by capturing spatial relationships between pixels?
A. Histogram equalization
B. Median filtering
C. Convolutional Neural Networks (CNNs)
D. Sobel operator
view answer:
C. Convolutional Neural Networks (CNNs)
Explanation:
Convolutional Neural Networks (CNNs) capture spatial relationships between pixels, making them effective in image classification.
12.
What is the primary role of "hyperparameter tuning" in image classification?
A. Noise reduction
B. Color correction
C. Optimizing the model's hyperparameters for better performance
D. Image resizing
view answer:
C. Optimizing the model's hyperparameters for better performance
Explanation:
Hyperparameter tuning optimizes the model's hyperparameters for better performance in image classification.
13.
What is the primary advantage of using "ensemble methods" in image classification?
A. Improved image resizing capabilities
B. Noise reduction
C. Enhanced accuracy and robustness through the combination of multiple models
D. Precise color correction
view answer:
C. Enhanced accuracy and robustness through the combination of multiple models
Explanation:
Ensemble methods enhance accuracy and robustness in image classification by combining multiple models.
14.
Which evaluation metric is commonly used to assess the performance of image classification models by considering both true positives and false positives?
A. Precision
B. Recall
C. Mean Squared Error (MSE)
D. F1 Score
view answer:
A. Precision
Explanation:
Precision is commonly used to assess image classification models by considering both true positives and false positives.
15.
In image classification, what is the primary goal of "feature selection"?
A. Noise reduction
B. Color correction
C. Reducing the dimensionality of the feature space while preserving relevant information
D. Image resizing
view answer:
C. Reducing the dimensionality of the feature space while preserving relevant information
Explanation:
Feature selection in image classification aims to reduce the dimensionality of the feature space while preserving relevant information.
16.
Which image classification method is based on the idea of using a "support vector machine" (SVM) as the classifier?
A. Histogram equalization
B. Median filtering
C. Sobel operator
D. SVM-based classification
view answer:
D. SVM-based classification
Explanation:
SVM-based classification uses a Support Vector Machine (SVM) as the classifier in image classification.
17.
What is the primary purpose of "cross-validation" in assessing the performance of image classification models?
A. Image resizing
B. Noise reduction
C. Evaluating the model's generalization ability and minimizing overfitting
D. Color correction
view answer:
C. Evaluating the model's generalization ability and minimizing overfitting
Explanation:
Cross-validation is used to evaluate the model's generalization ability and minimize overfitting in image classification.
18.
In image classification, what is the primary advantage of using "batch normalization" in deep neural networks?
A. Improved image resizing capabilities
B. Noise reduction
C. Faster convergence and improved training stability
D. Precise color correction
view answer:
C. Faster convergence and improved training stability
Explanation:
Batch normalization in deep neural networks leads to faster convergence and improved training stability in image classification.
19.
Which image classification approach is focused on classifying images based on textual descriptions?
A. Histogram equalization
B. Text-to-Image classification
C. Median filtering
D. Sobel operator
view answer:
B. Text-to-Image classification
Explanation:
Text-to-Image classification classifies images based on textual descriptions in image classification.
20.
What is the primary challenge in image classification when dealing with image occlusions and partial visibility?
A. Image resizing
B. Color correction
C. Noise reduction
D. Partial object recognition
view answer:
D. Partial object recognition
Explanation:
The primary challenge in image classification with image occlusions is partial object recognition.
21.
Which evaluation metric is used to assess the performance of image classification models by measuring the ability to correctly classify all positive samples?
A. Precision
B. Recall
C. Mean Absolute Error (MAE)
D. F1 Score
view answer:
B. Recall
Explanation:
Recall measures the ability to correctly classify all positive samples in image classification.
22.
What is the primary purpose of "multi-label classification" in image classification?
A. Image resizing
B. Color correction
C. Noise reduction
D. Assigning multiple labels or categories to a single image
view answer:
D. Assigning multiple labels or categories to a single image
Explanation:
Multi-label classification assigns multiple labels or categories to a single image in image classification.
23.
In image classification, what does "top-1 accuracy" refer to?
A. Noise reduction
B. Image resizing
C. The percentage of images for which the correct class is the top prediction
D. Color correction
view answer:
C. The percentage of images for which the correct class is the top prediction
Explanation:
Top-1 accuracy is the percentage of images for which the correct class is the top prediction in image classification.
24.
What is the primary goal of image classification in computer vision?
A. Image resizing
B. Noise reduction
C. Categorizing images into predefined classes or labels
D. Color correction
view answer:
C. Categorizing images into predefined classes or labels
Explanation:
Image classification aims to categorize images into predefined classes or labels based on their content.
25.
Which deep learning architecture is known for its success in image classification, particularly in the ImageNet Large Scale Visual Recognition Challenge?
A. ResNet
B. Histogram equalization
C. Median filtering
D. Sobel operator
view answer:
A. ResNet
Explanation:
ResNet is known for its success in image classification, especially in the ImageNet competition.
26.
In image classification, what is the primary purpose of the "softmax" activation function in the output layer of a neural network?
A. Noise reduction
B. Image resizing
C. Converting raw scores into class probabilities
D. Color correction
view answer:
C. Converting raw scores into class probabilities
Explanation:
The softmax activation function converts raw scores into class probabilities in the output layer of a neural network for image classification.
27.
Which type of data augmentation technique is commonly used in image classification to increase the size of the training dataset and improve model generalization?
A. Histogram equalization
B. Data augmentation
C. Median filtering
D. Color correction
view answer:
B. Data augmentation
Explanation:
Data augmentation is used to increase the size of the training dataset and improve model generalization in image classification.
28.
What is the primary role of "feature extraction" in image classification?
A. Image resizing
B. Noise reduction
C. Identifying and capturing discriminative features from images
D. Color correction
view answer:
C. Identifying and capturing discriminative features from images
Explanation:
Feature extraction in image classification identifies and captures discriminative features from images.
29.
Which popular image classification dataset contains a large collection of images across 1,000 different categories?
A. COCO (Common Objects in Context)
B. ImageNet
C. MNIST
D. CIFAR-10
view answer:
B. ImageNet
Explanation:
ImageNet is a popular image classification dataset containing a large collection of images across 1,000 different categories.
30.
What is the primary challenge in image classification when dealing with variations in lighting conditions and viewpoints?
A. Color correction
B. Image resizing
C. Illumination invariance
D. Noise reduction
view answer:
C. Illumination invariance
Explanation:
The primary challenge in image classification with lighting and viewpoint variations is achieving illumination invariance.
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