Incremental Learning from a Single Seed Image for Object Detection

Abstract

In this paper, we propose a novel online multiobject learning and detection algorithm. From single seed images of the target objects, our algorithm detects these objects in the input sequence, and incrementally updates the databases with the detection results. Reasonably sized databases are maintained as graphs of the registered images, while new views of the objects are added as the detection proceeds. The importance of the registered images is computed using our ranking algorithm, and redundant images are pruned from the database. The proposed method fully utilizes graphical representation to detect and recognize objects. A 3D model of a candidate object is built on-the-fly using the retrieved images, and initially undetected features are hallucinated for further matching and verification. This process improves the detection performance compared to the baseline algorithm. Object/background feature classification and object-likelihood maps effectively keep noisy background features from being added to the databases. The experimental results demonstrate that the proposed algorithm efficiently maintains the object databases and achieves better performance.