Indoor Room Recognition Menggunakan Multiple Instance Learning Convolutional Neural Networks - Yehezkiel Wuisang; Djoni Haryadi Setiabudi; Alvin Nathaniel Tjondrowiguno (Jurnal Infra, 2021)
Environment recognition is a modern problem that appears in this modern era. One of them is how a room’s type can be identified. Indoor room is a very challenging environment to identify because the identity of a room is represented by various types of objects in the room which by itself have various sizes and shapes. With the development of technology, especially machine learning, the type of room can be recognized automatically by a system with the help of Image Processing and Artificial Neural Network. This study uses the Mean-Shift algorithm to segment images and the Convolutional Neural Network (CNN) method assisted by the application of Multiple Instance Learning (MIL) so as to form the Multiple Instance Learning Convolutional Neural Network (MILCNN) method to identify room types. During training and testing, adjustments will be made to the method so that it can be applied in recognizing room types only through image labels without looking for individual object labels on images. This study classifies the room that contains an image by recognizing the features of the objects in it. The final result from testing the dataset produces a classification accuracy percentage that reaches 43.05%.