Computer Engineering Commons^™

On The Pooling Of Positive Examples With Ontology For Visual Concept Learning, Shiai Zhu, Chong-Wah Ngo, Yu-Gang Jiang

Research Collection School Of Computing and Information Systems

A common obstacle in effective learning of visual concept classifiers is the scarcity of positive training examples due to expensive labeling cost. This paper explores the sampling of weakly tagged web images for concept learning without human assistance. In particular, ontology knowledge is incorporated for semantic pooling of positive examples from ontologically neighboring concepts. This effectively widens the coverage of the positive samples with visually more diversified content, which is important for learning a good concept classifier. We experiment with two learning strategies: aggregate and incremental. The former strategy re-trains a new classifier by combining existing and newly collected examples, …