Indoor Scene Recognition via Object Detection and TF-IDF

Heikel, Edvard; Espinosa-Leal, Leonardo

doi:10.20944/preprints202207.0070.v1

2022

DOI: 10.20944/preprints202207.0070.v1

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Indoor Scene Recognition via Object Detection and TF-IDF

Edvard Heikel¹,

Leonardo Espinosa-Leal²

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. Th… Show more

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Cited by 4 publications

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Indoor Scene Recognition: An Attention-Based Approach Using Feature Selection-Based Transfer Learning and Deep Liquid State Machine

Surendran,

Chihi,

Anitha

et al. 2023

Algorithms

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Scene understanding is one of the most challenging areas of research in the fields of robotics and computer vision. Recognising indoor scenes is one of the research applications in the category of scene understanding that has gained attention in recent years. Recent developments in deep learning and transfer learning approaches have attracted huge attention in addressing this challenging area. In our work, we have proposed a fine-tuned deep transfer learning approach using DenseNet201 for feature extraction and a deep Liquid State Machine model as the classifier in order to develop a model for recognising and understanding indoor scenes. We have included fuzzy colour stacking techniques, colour-based segmentation, and an adaptive World Cup optimisation algorithm to improve the performance of our deep model. Our proposed model would dedicatedly assist the visually impaired and blind to navigate in the indoor environment and completely integrate into their day-to-day activities. Our proposed work was implemented on the NYU depth dataset and attained an accuracy of 96% for classifying the indoor scenes.

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Indoor Scene Recognition: An Attention-Based Approach Using Feature Selection-Based Transfer Learning and Deep Liquid State Machine

Surendran,

Chihi,

Anitha

et al. 2023

Algorithms

View full text Add to dashboard Cite

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

Khan,

Othman

2024

Computers

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Indoor scene classification plays a pivotal role in enabling social robots to seamlessly adapt to their environments, facilitating effective navigation and interaction within diverse indoor scenes. By accurately characterizing indoor scenes, robots can autonomously tailor their behaviors, making informed decisions to accomplish specific tasks. Traditional methods relying on manually crafted features encounter difficulties when characterizing complex indoor scenes. On the other hand, deep learning models address the shortcomings of traditional methods by autonomously learning hierarchical features from raw images. Despite the success of deep learning models, existing models still struggle to effectively characterize complex indoor scenes. This is because there is high degree of intra-class variability and inter-class similarity within indoor environments. To address this problem, we propose a dual-stream framework that harnesses both global contextual information and local features for enhanced recognition. The global stream captures high-level features and relationships across the scene. The local stream employs a fully convolutional network to extract fine-grained local information. The proposed dual-stream architecture effectively distinguishes scenes that share similar global contexts but contain different localized objects. We evaluate the performance of the proposed framework on a publicly available benchmark indoor scene dataset. From the experimental results, we demonstrate the effectiveness of the proposed framework.

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Indoor Scene Recognition via Object Detection and TF-IDF

Heikel

Espinosa-Leal

2022

J. Imaging

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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.

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Indoor Scene Recognition via Object Detection and TF-IDF

Cited by 4 publications

References 15 publications

Indoor Scene Recognition: An Attention-Based Approach Using Feature Selection-Based Transfer Learning and Deep Liquid State Machine

Indoor Scene Recognition: An Attention-Based Approach Using Feature Selection-Based Transfer Learning and Deep Liquid State Machine

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

Indoor Scene Recognition via Object Detection and TF-IDF

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