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An input image can be converted into structured form through ___________ Image Analysis CNN is a algorithm in which it comes under Deep Learning Confusion Matrix is not a technique used to evaluate the performance of a classifier FALSE Conventional classification algorithms on image data could not give significant accuracy Identify the unstructured data from the following. Both image and video clip The major steps involved in image classification are ___________ Input Image -> Preprocessing -> Feature Extraction -> Classification Noise can be removed using Data Preprocessing TRUE The data is split into _________ sets 3 The major function of PCA is to decompose a ________________________ into a set of successive orthogonal components Multivariate dataset Cross validation gives high variance if the testing set and training set are not drawn from the ______________________ Same Population A normalized image has ___________ Mean = 0 and variance = 1 SIFT is mainly used for images that are ___________ less simpled and less organised SVD is used in many fields TRUE For SVM it is good to have ___________ number of dimensions > the number of samples Normalization is the process of converting ___________ pixel values to normal state The main aim of using SIFT for feature extraction is to obtain features that are very sensitive to changes in scale, rotation, image resolution, illumination, etc. FALSE The scale-invariant feature transform can be used to detect and describe local features in images. TRUE Each class in CIFAR -10 dataset has _________ images. 6000 The data used to tune the model is _____________ Validation Set The number of incorrect predictions that the occurrence is negative is False Negative TRUE ___________________________ is defined as the percentage of correct predictions Classification Accuracy Naive Bayes algorithm comes under Deep Learning In the SVD method, a digital image is decomposed into ________ matrices 3 The process of changing the pixel intensity values to achieve consistency in dynamic range for images is ___________. Image normalisation The steps involved in building a classification model are - Initialize---->Train---->Predict---> Evaluate Data Preprocessing is a step the raw data is converted into a form suitable for subsequent analysis TRUE The main aim of whitening is to reduce data redundancy TRUE The main aim of using SIFT for feature extraction is to obtain features that are very sensitive to changes in scale, rotation, image resolution, illumination, etc. FALSE SVM is efficient on Clear Margin Seperation Each layer is composed of ___________, where the computation happens nodes CIFAR-10 image dataset consists of ____________ training data and _____________ testing data 50k 10k PCA and SVD are dimensionality reduction techniques TRUE Cross Validation is performed on ________________________ Unknown Data ZCA stands for ___________ Zero Phase Component Analysis ________________________ is the number of correct predictions that the occurrence is positive False Negative x In cross validation, the number of samples used for training the model is _____________ decreased Data Partitioning will help us to know efficiency of model TRUE Neural network consistis of __________ different layers 3 Scikit Learn is an machine learning python package. True Allowing training data to be included in testing data will give actual performance results FALSE CNN is mainly used in Image Recognition SIFT stands for ________________/ Scale Invariant Feature Transform
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