Case Study: Ensemble Decision-Based Annotation of Unconstrained Real Estate Images
Miroslav Despotovic, Zedong Zhang, Eric Stumpe, Matthias, Zeppelzauer
TL;DR
This paper presents a semi-supervised, rule-based approach for annotating real estate images, providing insights into image content characteristics and implementation requirements.
Contribution
It introduces a novel semi-supervised annotation method specifically tailored for real estate images, highlighting practical insights and content analysis.
Findings
Understanding of content characteristics of real estate images
Identification of key requirements for practical implementation
Insights into class-specific image features
Abstract
We describe a proof-of-concept for annotating real estate images using simple iterative rule-based semi-supervised learning. In this study, we have gained important insights into the content characteristics and uniqueness of individual image classes as well as essential requirements for a practical implementation.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsImage Retrieval and Classification Techniques · Advanced Image and Video Retrieval Techniques · Remote Sensing and LiDAR Applications