# An Indoor Room Classification System for Social Robots via Integration of CNN and ECOC
## 2 Related Work  
Recognizing different rooms in a home environment based on their specific function is an important problem for social robots, and its solution not only facilitates seamless movement from one place to another, it is the basis of all other tasks, including assistance to humans inside the house or performing various functions in the context of the robot's overall tasks. An interaction with a human might be in the form of \"_Please go to the kitchen and bring a cup of water_\". This problem has attracted the attention of robotics researchers in the last decade, and several conventional machine learning methods have been employed to address room classification in indoor settings. One of the early studies reported by [PERSON]'s group in [11] was to address semantic place classification of indoor environments by extracting features from a laser range data using AdaBoost algorithm. The experiments were conducted in a real office environment using sequential binary classifiers for differentiating between room, corridor, doorway, and hallway. It was suggested that the sequential binary AdaBoost classifiers were much more accurate than multi-class AdaBoost. The study was further extended in References [12; 13] by extracting features from laser and camera for classifying six different places: doorways, a laboratory, a kitchen, a seminar room, and a corridor, as well as examining the effect of the Hidden Markov Model on the final classification. The same algorithm, i.e., AdaBoost, was trained in Reference [14] using SIFT features of online images for seven different rooms. It examined the performance of different number of classes and different possible pairs of classes, where the success of the average of binary classifiers was 77%. Robotics researchers also employed the well-known support vector machine (SVM) algorithm for the room classification problem using different sensors. In Reference [15], laser data was used to build a hierarchical model, in which the hierarchy is employed for training and testing SVMs to classify 25 living rooms, 6 corridors, 35 bathrooms, and 28 bedrooms. Although this study reported an accuracy of 84.38%, laser data generally do not provide rich information, and require substantial processing to extract useful features. In contrast, vision features are used in other studies in order to train SVMs. In Reference [16], a voting technique was used to combine 3D features to GIST 2D features, and these were used for training SVMs to classify six indoor places: bathrooms, bedrooms, eating places, kitchens, living rooms, and offices. Furthermore, SVM and Random Forests (RF) classifiers were used and compared in Reference [17] to classify five places: corridors, laboratories, offices, kitchens, and study rooms using RGB-D images from a Kinect sensor. Room detection has also been addressed as an unsupervised learning problem using unlabeled images. In Reference [18], SIFT features and 3D representation were used to extract convex spaces for clustering images based on similarities. In addition, stereo imagery was used in Reference [19] for room detection and modeling by fusing 2D features with geometry data acquired from pixel-wise stereo for representing 3D scenes. The study was completed by modeling walls, rooms, and doorways using many techniques of extracting features, depth diffusion, depth segmentation, and clustering in order to detect room functionalities. The problem has also been addressed from different perspectives, such as the study in Reference [20], in which the authors addressed the context-awareness problem for service robots by developing a system that identified 3D objects using online information. As we can note from previous research, the main drawback is the huge effort required to extract features. This weakness can be overcome by adopting a convolutional neural network (CNN) algorithm. CNN is a category of deep neural network that has demonstrated successful results in the field of computer vision, such as face recognition and object detection.