Multiple-instance Learning for Natural Scene Classiication

We investigate a method called Multiple-Instance learning for learning simple templates that capture the color and spatial properties of classes of natural scene images from a small set of examples. These templates encode a scene class as image patches with color and spatial relations and can be used to classify a variety of natural scenes like elds, waterfalls and mountains among others. Example images are ambiguous since there are many possible templates that can describe an individual image. Multiple-Instance learning makes the ambiguity explicit, and we discuss the Diverse Density algorithm which is a method of learning from ambiguous examples. The system uses very low resolution images to extract the templates from a set of examples. Once a template is learned, we use the COREL photo library to test its retrieval rates and accuracy. We show that very simple templates are suu-cient, and that performance improves with more user interaction.