Hierarchical cluster guided labeling : efficient label collection for visual classification
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Visual classification is a core component in many visually intelligent systems. For example, recognition of objects and terrains provides perception during path planning and navigation tasks performed by autonomous agents. Supervised visual classifiers are typically trained with large sets of images to yield high classification performance. Although the collection of raw training data is easy, the required human effort to assign labels to this data is time consuming. This is particularly problematic in real-world applications with limited labeling time and resources. Techniques have emerged ...