Text-based indoor place recognition with deep neural network

Indoor place recognition is a challenging problem because of the hard representation to complicated intra-class variations and inter-class similarities.This paper presents a new indoor place recognition scheme using deep neural network. Traditional representations of indoor place almost utilize image feature to retain the spatial structure without considering the object's semantic characteristics. However, we argue that the attributes, state and relationships of objects are much more helpful in indoor place recognition. In particular, we improve the recognition framework by utilizing Place Descriptors (PDs) in text from to connect different types of place information with their categories. Meanwhile, we analyse the ability of Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) for classification in natural language, for which we use them to process the indoor place descriptions. In addition, we improve the robustness of the designed deep neural network by combining a number of effective strategies, i.e. L2-regularization, data normalization, and proper calibration of key parameters. Compared with existing state of the art, the proposed approach achieves well performance of 70.73%, 70.08% and 70.16% of accuracy, precision and recall on Visual Genome database respectively. Meanwhile, the accuracy becomes 98.6% after adding voting mechanics.