Hierarchical, Generic to Specific Multi-class Object Recognition

Abstract

Early vision systems could perform specific object recognition reasonably well but did not fare as well on identifying natural object classes. Recent advances have led to systems that can learn a representation for different object classes and achieve good generic object class recognition. However, these systems are generally unable to perform the fine distinctions required for specific object identification. It seems that the two approaches are in contrast with each other. We propose a system that addresses the problems of generic class recognition as well as specific object recognition in the same framework. Our system also possesses the property of graceful degradation, i.e., if it is unable to recognize an object as a dog, it recognizes it at least as a quadruped and so on. We automatically learn a hierarchy of the classes from the training data, progressing from the most generic class labels to the most specific object labels. This hierarchy is used during recognition. One important benefit of the hierarchical organization of the classes is that the number of comparisons performed for every input does not increase linearly with the number of classes added