lsq117/Defining-Public-Scenes-from-Private-Using-MIT-Indoor67

we mixed and matched several feature extraction methods and classifiers to separate public images from private ones. We compared the performance of image feature extraction tools, including Convolutional Neural Networks (CNNs), Scale-Invariant Feature Transform (SIFT), and VGG16. We also studied through several state-of-the-art classifiers, including Multilayer Perceptron (MLP), Support Vector Machine (SVM) and K-Means, Linear Discriminant Analysis (LDA), Random Forest (RF) to see which classifier works best with which feature extraction tool. Index Terms—Indoor Scene Classification, Transfer Learning, SIFT, CNN, VGG16, SVM

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