Classifying images using various algorithms after applying principal component analysis This Project involves Object Recognition along with the application of Principle Component Analysis. The Data set includes 10 objects which can be detected using various algorithms.
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The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images per class. * There are 50000 training images and 10000 test images.
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The dataset is divided into five training batches and one test batch, each with 10000 images.
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The test batch contains exactly 1000 randomly-selected images from each class.
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The training batches contain the remaining images in random order, but some training batches may contain more images from one class than another. Between them, the training batches contain exactly 5000 images from each class.
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These are the classes in the dataset:
airplane
automobile
bird
cat
deer
dog
frog
horse
ship
truck
The classes are completely mutually exclusive. i.e. There is no overlap between automobiles and trucks. "Automobile" includes sedans, SUVs, things of that sort. "Truck" includes only big trucks. Neither includes pickup trucks.
Out of all the algorithms used, SVM promises the best accuracy of 0.54.This can be improved further by using Neural Networks.