A framework for learning query concepts in image classification

In this paper, we adapt the Multiple Instance Learning paradigm using the Diverse Density algorithm as a way of modeling the ambiguity in images in order to learn "visual concepts" that can be used to classify new images. In this framework, a user labels an image as positive if the image contains the concept. Each example image is a bag of instances (sub-images) where only the bag is labeled-not the individual instances (sub-images). From a small collection of positive and negative examples, the system learns the concept and uses it to retrieve images that contain the concept from a large database. The learned "concepts" are simple templates that capture the color, texture and spatial properties of the class of images. We introduced this method earlier in the domain of natural scene classification using simple, low resolution sub-images as instances. In this paper, we extend the bag generator (the mechanism which takes an image and generates a set of instances) to generate more complex instances using multiple cues on segmented high resolution images. We show that this method can be used to learn certain object class concepts (e.g. cars) in addition, to natural scenes.