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In this paper, we propose a robust and parsimonious approach using DeepConvolutional Neural Network (DCNN) to recognize and interpret interior space.DCNN has achieved incredible success in object and scene recognition. In thisstudy we design and train a DCNN to classify a pre-zoning indoor space, andfrom a single phone photo to recognize the learned space features, with no needof additional assistive technology. We collect more than 600,000 images insideMIT campus buildings to train our DCNN model, and achieved 97.9% accuracy invalidation dataset and 81.7% accuracy in test dataset based on spatial-scalefixed model. Furthermore, the recognition accuracy and spatial resolution canbe potentially improved through multiscale classification model. We identifythe discriminative image regions through Class Activating Mapping (CAM)technique, to observe the model's behavior in how to recognize space andinterpret it in an abstract way. By evaluating the results withmisclassification matrix, we investigate the visual spatial feature of interiorspace by looking into its visual similarity and visual distinctiveness, givinginsights into interior design and human indoor perception and wayfindingresearch. The contribution of this paper is threefold. First, we propose arobust and parsimonious approach for indoor navigation using DCNN. Second, wedemonstrate that DCNN also has a potential capability in space feature learningand recognition, even under severe appearance changes. Third, we introduce aDCNN based approach to look into the visual similarity and visualdistinctiveness of interior space.

Indoor Space Recognition using Deep Convolutional Neural Network: A Case Study at MIT Campus