# Methods and Applications of Space Understanding in Indoor Environment--A Decade Survey
## 4 Taxonomy of High-Level Abstraction Category of Performed Process
### Taxonomy Presentation  
The second taxonomy groups input data types from Section 3 into buckets of the high-level abstraction category of performed processes. As articles using the same input data type completed different categories of tasks, each data structure could be assigned to more than one bucket simultaneously. Visualization of this taxonomy is presented in Figure 3. We distinguish four buckets of categories here: segmentation (without class recognition), segmentation with a simplified classification (room/corridor/unknown), segmentation with precise classification (kitchen/bathroom/etc.), and precise classification (without space segmentation). There was no additional splitting into smaller buckets as this would over-complicate the taxonomy. The separation criterion was the achieved level of semantic meaning assigned to the analyzed data.  
The **Segmentation** category groups solutions that detect building structures (rooms) in the source data but do not provide any deeper semantic meaning to it. These algorithms mainly separate areas and rooms from a more extensive context but without any details of what kind of room it is. All the segmented objects represented the same class, just a \"_room_\". As a result, there were, e.g., _room1_, _room2_, and _room3_ found. In multiple cases, the segmentation process took the form of data clustering.  
The **Segmentation + Simplified Classification** category expands on plain segmentation with no room-type analysis. It represents a bucket with solutions that recognize more than one class of object from the whole set of input data but without a complete semantic understanding of them. These solutions can determine simplified class separation, e.g., into two or three classes of rooms. As a result, there were, e.g., _room1_, _'corridor1'_, _'room2'_, and _'outside'_ found.  
Figure 3: Diagram presenting the second constructed taxonomy—based on the high-level abstraction category of found solutions.  
The **Segmentation + Precise Classification** category groups solutions that further expand the functionalities of methods from previous buckets. This time the methods can not only segment rooms but also recognize their precise function. They add semantic meaning to each segmented object from a broad set of class labels. These solutions can specify that the found room was a kitchen, living room, bedroom, bathroom, etc. The number of known classes varied between solutions.  
The **Precise Classification** bucket groups methods that implement multi-class classification with a wide range of available class labels but without their segmentation from the whole set of input data. Previously described segmentation methods could detect multiple instances of room structures in the input data and assign meaning to them. Methods from this bucket are entirely focused only on the second part, i.e., just on the class assignment. An example could be the room-type classification as presented in the input picture taken with a smartphone. The class was assigned to the entire input and not to separate fragments of it. A sample result was a classification: \"this is a kitchen\". Table 3 presents a matrix of referenced articles, but this time organized by the processed input data type and the high-level solution category.