What Looks Good with my Sofa: Multimodal Search Engine for Interior Design
This addresses the need for efficient interior design recommendations for users, but it is incremental as it builds on existing multimodal search techniques.
The paper tackles the problem of retrieving interior design items that match visual and aesthetic criteria by proposing a multimodal search engine that blends visual and textual queries, achieving an 11% increase in average style similarity score.
In this paper, we propose a multi-modal search engine for interior design that combines visual and textual queries. The goal of our engine is to retrieve interior objects, e.g. furniture or wall clocks, that share visual and aesthetic similarities with the query. Our search engine allows the user to take a photo of a room and retrieve with a high recall a list of items identical or visually similar to those present in the photo. Additionally, it allows to return other items that aesthetically and stylistically fit well together. To achieve this goal, our system blends the results obtained using textual and visual modalities. Thanks to this blending strategy, we increase the average style similarity score of the retrieved items by 11%. Our work is implemented as a Web-based application and it is planned to be opened to the public.