Automatic room information retrieval and classification from floor plan using linear regression model
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ORIGINAL PAPER
Automatic room information retrieval and classification from floor plan using linear regression model Hiren K. Mewada1
· Amit V. Patel2 · Jitendra Chaudhari2 · Keyur Mahant2 · Alpesh Vala2
Received: 10 December 2019 / Revised: 8 June 2020 / Accepted: 18 July 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract The automatic creation of a repository of the building’s floor plan helps a lot to the architects to reuse them. The basic approach is to extract and recognize texts, symbols or graphics to retrieve the information of the floor plan from the images. This paper proposes a floor plan information retrieval algorithm. The proposed algorithm is based on shape extraction and room identification. α-shape is used for finding an accurate shape. From the detected shapes, actual areas of rooms are calculated. Later, a regression model-based binary room classification model is proposed to classify them into room-type, i.e., bedroom, drawing room, kitchen, and non-room-type, i.e., parking porch, bathroom, study room and prayer room. The proposed model is tested on the CVC-FP dataset with an average room detection accuracy of 85.71% and room recognition accuracy of 88%. Keywords Floor plan · Image retrieval · Regression model · α-shape · Document image analysis · Pattern recognition
1 Introduction A floor plan is a graphical representation of top view of a house or a building along with a necessary dimensions. Twodimensional floor plan evaluation and information retrieval can help in many applications, e.g., to generate 3D model visualization develop virtual-navigation inside the building, count the number of rooms and their area and architectural information recovery. It also provides details of the interior . Architects are required to customize floor plans to meet the client’s requirements. Currently, many architects resort to the tedious process of drawing up plans manually. This cumbersome burden can be significantly reduced with automation, e.g., retrieval of digital plans from a database. This can assure the re-utilization of the existing design for their projects. In addition, previously all floor plans were prepared on the drawing sheet and digitized through photographs. These photographs cannot be read by an application or algorithms,
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Hiren K. Mewada [email protected]
1
Electrical Engineering Department, Prince Mohammad Bin Fahd University, Al Khobar, Kingdom of Saudi Arabia
2
CHARUSAT Space Research and Technology Center, Charotar University of Science and Technology, Changa, Gujarat, India
thereby rendering them practically useless for further customization. The algorithms used for automated information retrieval from the floor plans can be divided into two categories: (a) text and label, symbol extraction from the floor plan, i.e., textual processing, and (b) topology connectivity identification and information retrieval, i.e., graphical processing. These two categories belong to image segmentation, layout formation and graph recognition. Identification of la
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