Automated Interior Designing using Bayesian networks
I’ll be presenting about an almost completely automated intelligent system that produces realistic and aesthetically appealing interior designs for homes. The particularly striking feature of our system is that it generates multiple plausible options for an empty room. The relationships between different elements of a room and items placed in the room are represented as Bayesian networks. The causal relationships defining the network structure are derived from standard thumbrules of interior designing. The parameters for every node in the network are learnt from information extracted semi-automatically from the top view images of furnished living rooms and conversation areas. New layouts based on user inputs are generated upon inferencing from this learnt network.
One can get a good idea of how to model problems using Bayesian networks and use it in other interesting domains.
I am research scholar at IIIT Bangalore. Love to apply machine learning to computer vision problems. Presenty duing my internship in one of the best AI based start ups in India- Snapshopr. I am more into Bayesian analytics and research. Two of my papers “On Learning mixed Bayesian Networks” and “Towards an Automated Home Interior Designer System” recently got accepted in international conferences. I would like to share a gist of that with everyone.