# Methods and Applications of Space Understanding in Indoor Environment--A Decade Survey
## 5 Taxonomy of Accomplished Tasks
### Taxonomy Presentation
#### 5.1.7 Room Classification  
In the room classification bucket, the analyzed solutions predicted the kind of room the given input represented. Again, this task covered all distinguished input data types and delivered numerous articles. Recognition of the room type can be achieved using an image of the indoor environment, calculated energy measurements, or even the recorded sound echo. The presented example (Figure 11) analyzed a raster image, searched for characteristic elements, and deducted the placement and types of rooms. Indoor pictures, as a sub-bucket of data types, was the largest one and was dominated by machine-learning approaches. [PERSON] et al. [152] presented a CNN-based solution for a situated robot's recognition of the environment around it. They combined picture understanding with a web mining approach. Room type was deducted from a set of object pictures and their co-occurrence in the web resources. Similarly, [PERSON] et al. [150] extracted parts of the pictures and analyzed them as regions of interest. Instead of a holistic categorization, they classified each part and deduced the end result, which appeared to be robust to image distortions. [PERSON] and [PERSON] [155] compared multiple CNN architectures in the room classification task for a humanoid robot exploring the environment. In their work, a custom multi-binary classifier was proposed and combined with a CNN for better performance. The article of [PERSON] et al. [149] was the only one avoiding a network of convolutions--they proposed and compared systems of Support Vector Machines (SVMs)  
Figure 10: Visualization of the Graph Generation task. Input data (recorded user trajectory with some understanding of the building, (**left**)) is used to construct a graph structure (**right**) representing rooms and areas as graph nodes, and their adjacency as graph edges. and Bayesian Network Classifiers (BNC). The reported results indicated that with the proper processing, BNCs outperformed SVM classifiers. The survey extension returned even more results than the main flow; we found 13 further articles. [PERSON] [159], in one of the oldest articles analyzed, processed images coming from a stream of measurements, like a moving robot capturing a video. Changepoint detection was performed sequentially in consecutive frames and used to classify entire video segments. One article analyzing the robot's camera orientation was the one by [PERSON] and [PERSON] [158]. In their article, they proposed a \"Bubble Space\" that represented the nodes of topological maps of an environment as abstract representations of the robot base and its viewing directions. [PERSON] et al. [162] used a readily available Kinect camera and presented its use on a mobile service robot. Combining the depth and grayscale images made it possible to gather data in which patterns could be detected. Histograms of patterns were then simplified into a feature vector used in a supervised indoor space classification method. A visible direction in the room classification techniques was to analyze not the whole image at once but part by part. [PERSON] et al. [160] presented a solution based on picture regions. They assumed that a picture of a specific class of objects has a set of regions expected to contain a particular data type. An example of this might be the blue sky in the top part of the beach image. With the use of a reconfigurable version of Bag of Words (BoW), their model learns interesting regions of images and uses them to classify the observed scene. Similarly, [PERSON] and [PERSON] [157] presented a solution based on the idea of spatial pyramid processing. Proposed Latent Pyramidal Regions (LPRs) were used to describe the characteristics of parts of the processed scene image. The later searching through of all possible subwindows of the input image allowed using learned representations for scene classification. A year later, [PERSON] et al. [163] proposed a solution combining the BoW technique with a novel approach called Bag of Parts (BoP).