Image Classification — MCQs | Digital Image Processing

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1. What is the primary goal of image classification in digital image processing?



2. Which of the following is a supervised learning technique used in image classification?



3. What does CNN stand for in image classification tasks?



4. Which layer in a CNN is primarily responsible for feature extraction?



5. What is the role of the pooling layer in a convolutional neural network?



6. Which pooling method is most commonly used in CNNs?



7. Which activation function is commonly used in deep learning for image classification?



8. What is the purpose of the softmax layer in a classification network?



9. Which term refers to the error between predicted and actual labels in classification?



10. Which loss function is commonly used in multi-class classification tasks?



11. Which method splits the dataset into training and testing sets?



12. Which of the following techniques improves generalization in CNNs?



13. Which metric is not typically used to evaluate classification performance?



14. Which approach increases dataset size by transformations?



15. Which classifier is commonly used for binary image classification problems?



16. What does the term “overfitting” mean in image classification?



17. Which layer type flattens the output in CNNs for classification?



18. Which algorithm uses labeled data for classification tasks?



19. Which term defines the number of filters in a convolutional layer?



20. Which technique is not a dimensionality reduction method used in classification?



21. What is the role of the ReLU function in CNNs?



22. Which CNN architecture is known for its deep structure with 19 layers?



23. Which network introduced the concept of residual learning?



24. What is transfer learning in the context of image classification?



25. Which layer is typically used at the end of a CNN for classification?



26. What does an epoch refer to in training a classification model?



27. Which evaluation metric is best when dealing with imbalanced datasets?



28. What is the main function of a classification layer in CNN?



29. Which CNN model won the ImageNet 2012 competition?



30. Which type of learning is image classification based on?



31. What is the main drawback of using high learning rates?



32. Which of the following is not a component of CNN?



33. Which of the following is a color image classification dataset?



34. Which image classification task has multiple labels per image?



35. Which of the following networks is best for real-time image classification?



36. Which CNN model uses depthwise separable convolutions?



37. Which evaluation metric measures the ratio of true positives to total predicted positives?



38. Which of the following is not a data preprocessing step?



39. Which approach is used to prevent overfitting?



40. What does the stride parameter in convolution determine?



41. Which dataset contains grayscale images of handwritten digits?



42. Which type of classification involves only two possible classes?



43. Which algorithm is best suited for non-linear image classification?



44. What is the major difference between CNN and traditional neural networks for images?



45. What is the output of a softmax function?



46. Which of the following models is designed for very deep networks?



47. Which dataset is largest among the following for classification?



48. Which CNN architecture introduced inception modules?



49. Which technique modifies model weights based on error feedback?



50. Which of the following is used to reduce the internal covariate shift during training in image classification?



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