Handling of Backgrounds in Furniture Recognition with Neural Networks

This work explores different object segmentation methods for the use-case of a visual furniture recommender system. The two main contributions are a novel method of synthetic furniture image generation to be used for semantic segmentation for different furniture classes and foreground-background separation. Secondly, an evaluation of the best performing segmentation network architectures in a recommender system. The synthetic dataset proves to be a suitable way to train image segmentation and to be a fitting way to represent real world data of furniture. The setup of the pipeline for generating the data provides a scalable and diverse data set through numerous augmentations. Evaluating the best performing segmentation network architectures in a visual furniture recommender system shows that significant improvements are achieved for recommendations compared to recommendations based on the recommender system without background subtraction. Recommendations, based on background subtraction, are chosen two to three times more often.