EE5731_Questions_updated_9-9

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Feature Selection in AdaBoost What is the recommended approach for feature selection in AdaBoost according to the lecture? (a) Select all features initially and remove the least important ones in each iteration. (b) Use all available features because AdaBoost can handle very high dimensional data. (c) Choose features randomly to ensure the model is robust and generalized. (d) Start with a reasonable subset of features, checking the error for all features with the same weight, and select those with the smallest error. Justify your answer. (d) Start with a reasonable subset of features, checking the error for all features with the same weight, and select those with the smallest error. Justification: While AdaBoost is capable of feature selection to some extent through the weights it assigns to each feature, it is generally a good practice to start with a feature set that has already been narrowed down based on some criterion, such as error in classification. AdaBoost can then further refine the selection by concentrating on the features that minimize the error. This approach helps in reducing overfitting and computational complexity. Testing Process in Face Detection For the testing process described in the AdaBoost face detection, which of the following statements are true? (a) The algorithm begins by testing the image with a simple 24x24 window to detect faces. (b) AdaBoost requires the input image to be in color to accurately detect faces. (c) The algorithm uses an integral image to speed up the process of generating subwindows. (d) The size of the subwindows is incrementally increased to detect faces of different sizes. Justify your answer. (a) The algorithm begins by testing the image with a simple 24x24 window to detect faces. Justification: A standard approach in face detection algorithms, such as the Viola-Jones object detection framework, which often uses AdaBoost, is to use a fixed- size sliding window that scans across the image. The 24x24 window size is a common choice because it is small enough for efficiency but large enough to capture necessary facial features. (c) The algorithm uses an integral image to speed up the process of generating subwindows. Justification: The integral image is a tool that allows for rapid feature evaluation. It is used in face detection algorithms to quickly calculate the sum of pixel values over rectangular regions, which significantly speeds up the feature extraction process. (d) The size of the subwindows is incrementally increased to detect faces of different sizes. Justification: To detect faces of various sizes, the algorithm scales the
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