AN ONTOLOGICAL BAGGING APPROACH FOR IMAGE CLASSIFICATION OF CROWDSOURCED DATA

In this paper, we study how to use semantic relationships for image classification in order to improve the classification ac-curacy. We achieve the goal by imitating the human visual system which classifies categories from coarse to fine grains based on different visual features. We propose an ontolog-ical bagging algorithm where most discriminative weak at-tributes are automatically learned for different semantic levels by multiple instance learning and the bagging idea is applied to reduce the error propagations of hierarchical classifiers. We also leverage ontological knowledge to augment crowdsourc-ing annotations (e.g., a hatchback is also a vehicle) in order to train hierarchical classifiers. Our method is tested on a vehi-cle dataset from the popular crowdsourcing dataset ImageNet. Experimental results show that our method not only achieves state-of-the-art results but also identifies semantically mean-ingful visual features. Index Terms — Ontology, image classification, hierarchi-cal weak attributes, crowdsourcin