  Object recognition systems usually require fully complete manually labeled
training data to train the classifier. In this paper, we study the problem of
object recognition where the training samples are missing during the classifier
learning stage, a task also known as zero-shot learning. We propose a novel
zero-shot learning strategy that utilizes the topic model and hierarchical
class concept. Our proposed method advanced where cumbersome human annotation
stage (i.e. attribute-based classification) is eliminated. We achieve
comparable performance with state-of-the-art algorithms in four public
datasets: PubFig (67.09%), Cifar-100 (54.85%), Caltech-256 (52.14%), and
Animals with Attributes (49.65%) when unseen classes exist in the
classification task.
