Fast online incremental approach of unseen place classification using disjoint-text attribute prediction

A new approach of unseen place classification in a commercial district is presented. It can classify input scenes into the correct place classes without the needs for sample images of places for training. The number of place classes and their definition are supervised by humans using text information only. A description of individual place classes is obtained from humans as a set of words that are regarded as the disjoint-text-attributes of the unseen place. During classification, our approach determines the number of text-attributes found in an image. Our approach runs in an online incremental manner in the sense that the description of place classes can be updated and a new place class can be added at any time. Our approach can be used, does not require any training dataset, and is available in multiple languages. The evaluation is done by a set of Google Street View images of a shopping area in Japan where both the Japanese and English languages are available. The result shows that the proposed method outperforms the state-of-the-art methods of scene text recognition and standard pattern recognition. The computation is sufficiently fast for real-time application.