Multi-Scale Feature Fusion for Interior Style Detection

Abstract

Text-based search engines can extract various types of information when a user enters an appropriate search query. However, a text-based search often fails in image retrieval when image understanding is needed. Deep learning (DL) is often used for image task problems, and various DL methods have successfully extracted visual features. However, as human perception differs for each individual, a dataset with an abundant number of images evaluated by human subjects is not available in many cases, although DL requires a considerable amount of data to estimate space ambiance, and the DL models that have been created are difficult to understand. In addition, it has been reported that texture is deeply related to space ambiance. Therefore, in this study, bag of visual words (BoVW) is used. By applying a hierarchical representation to BoVW, we propose a new interior style detection method using multi-scale features and boosting. The multi-scale features are created by combining global features from BoVW and local features that use object detection. Experiments on an image understanding task were conducted on a dataset consisting of room images with multiple styles. The results show that the proposed method improves the accuracy by 0.128 compared with the conventional method and by 0.021 compared with a residual network. Therefore, the proposed method can better detect interior style using multi-scale features.

Optimization of table tennis target detection algorithm guided by multi-scale feature fusion of deep learning

“…According to the findings of the experiments, this approach is more accurate at detecting and locating more challenging items. A brand-new indoor style recognition technique employing multi-scale characteristics and lifting 24 was put forth by Yaguchi et al (2022). The results showed that the accuracy of the suggested strategy had increased by 0.021 compared to the residual network and by 0.128 compared to the conventional method.…”