Multiple-Instance Learning for Natural Scene Classification

Multiple-Instance learning is a way of mod-eling ambiguity in supervised learning examples. Each example is a bag of instances, but only the bag is labeled-not the individual instances. A bag is labeled negative if all the instances are negative, and positive if at least one of the instances in positive. We apply the Multiple-Instance learning framework to the problem of learning how to classify natural images. Images are inherently ambiguous since they can represent many diierent things. A user labels an image as positive if the image somehow contains the concept. Each image is a bag, and the instances are various sub-regions in the image. From a small collection of positive and negative examples , we can learn the concept and then use it to retrieve images that contain the concept from a large database. We show that the Diverse Density algorithm performs well in this task, that simple hypothesis classes are suucient to classify natural images, and that user interaction helps to improve performance .