Convolutional neural networks as feature extractors for data-scarce visual searches
ben Abdallah, Hichem
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Image classification is one of the core problems in Computer Vision. The classification task consists of predicting a single label from a fixed set of categories for a single image. To perform an image classification, the classifier should consider the semantic identity of the image rather than irrelevant characteristics and variations such as the coincidental contrast or brightness of the images or the type of background. Applying Convolutional Neural Networks (CNNs) as feature extractors is a powerful approach to image classification. Training these CNNs necessitates a tremendous amount of training samples, and it is costly in terms of computational time. Since it is not guaranteed that one can find a sufficient amount of training data for a specific class target, we are conducting transfer learning of a CNN model (learned from a large data set) to generate a new representation of the images. These representations are classified with K-Nearest Neighbors within a target space that has just a few training samples.We aim to define the appropriate parameters including distance metric, layer from which to extract features, and minimum number of training samples to be considered to obtain the best classification results with our approach.
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