How Many Bedrooms Do You Need? A Real-Estate Recommender System from Architectural Floor Plan Images

This paper introduces an automated image processing method to analyze an architectural floor plan database. The floor plan information, such as the measurement of the rooms, dimension lines, and even the location of each room, can be automatically produced. This assists the real-estate agents to maximise the chances of the closure of deals by providing explicit insights to the prospective purchasers. With a clear idea about the layout of the place, customers can quickly make an analytical decision. Besides, it reduces the specialized training cost and increases the efficiency in business actions by understanding the property types with the greatest demand. Succinctly, this paper utilizes both the traditional image processing and convolutional neural networks (CNNs) to detect the bedrooms by undergoing the segmentation and classification processes. A thorough experiment, analysis, and evaluation had been performed to verify the effectiveness of the proposed framework. As a result, a three-class bedroom classification accuracy of ∼ 90% was achieved when validating on more than 500 image samples that consist of the different room numbers. In addition, qualitative findings were presented to manifest visually the feasibility of the algorithm developed.