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Automatic Image Annotation via Combining Low-level Colour Feature with Features Learned from Convolutional Neural Networks
2018
NeuroQuantology
In this paper, a feature combination approach to annotate and retrieve images is proposed. In addition to using low-level colour features from original images, we extract features learned from convolutional neural networks (CNNs). We find these two sets are complementary to each other in conducting automatic image annotation (AIA). For both single-label CIFAR-10 and multi-label COREL-5K AIA tasks, the CNN-learned features perform slightly better than the low-level image features. Finally, when
doi:10.14704/nq.2018.16.6.1612
fatcat:f3hspon52bbefndeacycpoim6i