This paper presents a novel framework for online incremental place recognition in an indoor environment. The framework addresses the scenario in which scene images are gradually obtained during long-term operation in the real-world indoor environment. Multiple users may interact with the classification system and confirm either current or past prediction results; the system then immediately updates itself to improve the classification system. This framework is based on the proposed \emph{n}-value self-organizing and incremental neural network (\emph{n}-SOINN), which has been derived by modifying the original SOINN to be appropriate for use in scene recognition. The evaluation was performed on the standard MIT 67-category indoor scene dataset and shows that the proposed framework achieves the same accuracy as that of the state-of-the-art offline method, while the computation time of the proposed framework is significantly faster and fully incremental update is allowed. Additionally, a small extra set of training samples is incrementally given to the system to simulate the incremental learning situation. The result shows that the proposed framework can leverage such additional samples and achieve the state-of-the-art result.

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.