Online Learning of Discriminative Patterns from Unlimited Sequences of Candidates
Recent research in object recognition has demonstrated the advantages of representing objects and scenes through localized patterns such as small image templates. In this paper we study the selection of patterns in the framework of extended supervised online learning, where not only new examples but also new candidate patterns become available over time. We propose an algorithm that maintains a pool of discriminative patterns and improves the quality of the pool in a disciplined manner over time. The proposed algorithm is not tied to any specific pattern type or data domain. We evaluate the method on several object detection tasks.