Visualization of high dimensional image features for classification
2016 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)(2016)
Abstract
In image classification tasks, the image is rarely represented as only a collection of raw pixels. Myriad alternative representations, from Gaussian kernels to bags-of-words to layers of a convolutional neural network, have been proposed both to decrease the dimensionality of the task and, more importantly, to move into a space which better facilitates classification. This work explores several methods for evaluating the suitability of the high-dimensional image representations produced by the layers of a convolutional neural network. Quantitative methods are considered along with visualization techniques. These visualizations and metrics aid in understanding what classifiers might be better suited to a given dataset. We demonstrate these methods on the MNIST handwritten digit dataset and compare the results with accuracies obtained from classifiers.
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Key words
high dimensional image features,image classification tasks,myriad alternative representations,Gaussian kernels,bags-of-words,convolutional neural network,image representations,visualization techniques,MNIST handwritten digit dataset
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