Indoor Scene Recognition via Object Detection and TF-IDF

Abstract

Indoor scene recognition and semantic information can be helpful for social robots. Recently, in the field of indoor scene recognition, researchers have incorporated object-level information and shown improved performances. This paper demonstrates that scene recognition can be performed solely using object-level information in line with these advances. A state-of-the-art object detection model was trained to detect objects typically found in indoor environments and then used to detect objects in scene data. These predicted objects were then used as features to predict room categories. This paper successfully combines approaches conventionally used in computer vision and natural language processing (YOLO and TF-IDF, respectively). These approaches could be further helpful in the field of embodied research and dynamic scene classification, which we elaborate on.

Tbexplain: A Text-Based Explanation Method for Scene Classification Models with the Statistical Prediction Correction

“…Chen et al [49] considered the objects as the context in the scene and proposed to derive object embeddings by implementing an object segmentation module, and leveraged these vectors to refine the top-5 predictions of ResNet [5] to improve scene recognition. On the other hand, Heikel et al [50] used YOLO as an object detection architecture to extract objects from the scene. They further mapped these objects to TF-IDF feature vectors and utilized them for training a scene classifier to predict scene categories.…”

Indoor Scene Classification through Dual-Stream Deep Learning: A Framework for Improved Scene Understanding in Robotics

“…Soroush et al [29] presented new fusion techniques for scene recognition and classification, utilizing both NIR and RGB sensor data. Heikel et al [30] presented a novel approach by employing an object detector (YOLO) to detect indoor objects, which are then used as features for predicting room categories. Deep learning models are data-driven, necessitating large amounts of data for training.…”

A New Method for Classifying Scenes for Simultaneous Localization and Mapping Using the Boundary Object Function Descriptor on RGB-D Points

“…In recent years, the application of neural networks, in particular those with a deep learning architecture, in the field of scene classification has witnessed a significant increase. Heikel and Espinosa-Leal [14] implemented a YOLO-based object detector that gives a descriptor of each image this was put in Tf-idf representation; finally, the information was classified using random forest. The pipeline is similar to ours, with the difference being that we use a support vector machine for classification and BOF as the descriptor.…”