In recent past, there has been a steep increase in the use of online platforms for the search of desired products. Real estate industry is no exception and has started initiating rent/sale of houses through online platforms. In this paper, we propose a deep neural network framework to facilitate automatic search of homes based on their floor plans. The salient features of this framework are that the query can be either an image (existing floor plan) or a sketch through a sketch pad interface. Our proposed framework automatically determines the type of query (image or sketch) and retrieves similar floor plan images from the database. The critical contributions of our proposed approach are: (1) a novel unified floor plan retrieval framework using multimodal query, i.e., an intuitive and convenient sketch query mode as well as a query by example mode ; (2) a conjunction of autoencoder, Cyclic GAN and CNN for the task of domain mapping and floor plan image retrieval. We have reported results of extensive experimentation and comparison with baseline results to establish the effectiveness of our approach.

中文翻译：

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基于深度神经网络的多特征平面图检索新特征转换框架

最近，使用在线平台搜索所需产品的数量急剧增加。房地产行业也不例外，它已开始通过在线平台进行房屋的出租/出售。在本文中，我们提出了一个深度神经网络框架，以促进根据房屋平面图自动搜索房屋。该框架的突出特点是查询可以是图像（现有平面图），也可以是通过草图板界面生成的草图。我们提出的框架会自动确定查询的类型（图像或草图），并从数据库中检索相似的平面图图像。我们提出的方法的关键贡献是：（1）使用多模式查询的新颖的统一平面图检索框架，即 直观方便的草图查询模式以及示例查询模式; （2）自动编码器，循环GAN和CNN的结合，用于域映射和平面图图像检索的任务。我们已经报告了广泛实验的结果，并与基线结果进行了比较，以确立我们方法的有效性。