Image feature associations via local semantic structure

Abstract

Research in the field of object recognition suffers from two distinct weaknesses that limits its effectiveness in natural environments. The first is that this research tends to rely on labeled training images, or other forms of supervision, to learn object models and recognize these models in novel images, thus preventing the learning of objects that are not labeled by humans. The second is that such systems tend to assume that the goal is to recognize a single, dominant foreground object. This research implements a different method of object recognition that learns, with- out supervision, which object(s) are in natural scenes. This approach uses the semantic co-occurance information of local image features to form object models from groups of image features, which shall be called percepts. These percepts are then used to recognize objects in novel images. It will be shown that this approach is capable of learning object categories without supervision and recognition in complex multi-object scenes. It will also be shown that this approach out-performs a nearest-neighbor scene recognition approach.