# 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 their work, attention is placed on the part-learning process. Detection of important parts of the picture is achieved automatically and is used without extensive user involvement. Hierarchical dependencies between parts of the pictures were also analyzed by [PERSON] and [PERSON] [161]. They assumed each scene was built using some substructures, separated objects, and connections between them. In their work, a Minimum Description Length (MDL) principle was used to deduce the scene type from a set of its building blocks. In this way, a scene could be classified as a kitchen if its recognized components were chairs next to tables and cabinets under the stove. [PERSON] et al. [164] proposed a novel local descriptor for low-level image representation, significantly different from the solutions mentioned above, i.e., Oriented Texture Curves (OTC). In OTC processing, each patch of the image was analyzed for its texture and color variation along different orientations. Constructed curves were processed into a single descriptor that was used for scene classification. A lower level of processing was proposed by [PERSON] et al. [165]. Instead of processing subparts, they prepared a pixel-level-based solution named Discriminative and Shareable Feature Learning (DSFL). Their main idea was to generate a bank of filters that represent common features of images and encode information from all raw pixel values. In a typical analyzed picture, only a specific subset of all the features was present, which allowed for scene classification. The opposite approach, the holistic analysis of pictures, was presented by [PERSON] et al. [169]. They presented a solution modeling the scene as a bag of object semantics achieved by a CNN trained on the ImageNet dataset. A constructed Fisher Vector (FV) summarizing object descriptors was reported to present better performance than an FV of low-level features. Similarly, the use of CNNs was further researched by [PERSON] and [PERSON] [166]. Aware of CNN's sensitivity to image distortions, they focused on proposing a multilevel processing pipeline that fine-tuned the networks in a cascade approach, relying on differently scaled input images. Cross-level processing has been reported to improve the robustness to scale transformation of pictures. The lack of adequate datasets dedicated to scene recognition was addressed by [PERSON] et al. [168]. They presented the Places dataset consisting of over 7 million labeled pictures of scenes. While presenting the dataset, they also prepared a solution called Places-CNN and reported competitive performance. On the other hand, [PERSON] et al. [167] presented a solution focusedon unsupervised learning. Based on the image detectors trained on popular image datasets, they studied the possibility of higher-level feature processing on top of the returned object representations. Graph data were processed by three of the found papers. The oldest one, by [PERSON] et al. [81], was from 2017. They analyzed the graph structure of a building using Statistical Relational Learning (SRL). The KLog [211] algorithm was reported to be successfully applied to room and building type classification. [PERSON] et al. [84] presented graph processing techniques with an extended comparison of various Graph Neural Networks (GNNs) analyzing floor plan graphs. GraphSAGE [212] and Topology Adaptive Graph Convolutional Network (TAGCN) [213] were reported to outperform solutions using the Graph Convolutional Network (GCN) [214], Graph Attention Network (GAT) [215], and Multi-Layer Perceptron (MLP). The latest research by [PERSON] et al. [85] continued the idea and proposed an improved version of the GraphSAGE algorithm called SAGE-E. It used graph node features and edge features, allowing it to outperform other solutions. Three-dimensional spatial data were the least represented. [PERSON] and [PERSON] [15] investigated the possibility of point cloud processing using a novel spatial feature vector describing the properties of the environment.