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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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| Paper Title: | SceneScribe: Automating Video Annotation with Textual Descriptions |
| Authors Name: | Neha B A , Dr. Bhagyashree Ambore , Ganesh Sharma , Shreyash Saxena , Sujay Karanth |
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IJRTI_189876
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| Published Paper Id: | IJRTI2405049 |
| Published In: | Volume 9 Issue 5, May-2024 |
| DOI: | |
| Abstract: | Background: In this manuscript, we introduce SceneScribe, a versatile semi-automatic tool designed for annotating scene graphs within images. SceneScribe empowers human annotators by providing them with the capability to articulate and depict the intricate relationships among participants observed within visual scenes, effectively constructing directed graphs. This functionality serves as a foundational pillar for various downstream tasks in computer vision, including but not limited to image captioning, Visual Question Answering (VQA), and scene graph generation, fostering enhanced learning and reasoning capabilities in these domains. Method: SceneScribe offers unparalleled flexibility in managing annotations across diverse image datasets. Annotators can choose to consolidate annotations into a single VG150 data-format file, seamlessly integrating with various scene graph models for enhanced compatibility. Alternatively, annotations can be segmented into separate files for individual images, enabling the creation of custom datasets tailored to specific research goals. Additionally, SceneScribe includes an intelligent rule-based algorithm that recommends relationships based on predefined criteria, streamlining the annotation process and reducing the burden associated with manual efforts. This combination of features enhances efficiency and productivity in scene graph annotation tasks. Results: To demonstrate the practical utility and effectiveness of SceneScribe, we introduce Traffic Genome, a comprehensive scene graph dataset consisting of 1000 diverse traffic images. Through the creation and annotation of Traffic Genome using SceneScribe, we showcase the software's capability to facilitate accurate and efficient scene graph annotation across a range of real-world scenarios and applications. Conclusion: Overall, SceneScribe represents a valuable tool in the realm of computer vision, offering a user-friendly and adaptable solution for annotating scene graphs in images, thereby advancing research and development in visual understanding and reasoning tasks. |
| Keywords: | Scene Graphs, Annotations, Relationships, Computer Vision, Automated Annotation System |
| Cite Article: | "SceneScribe: Automating Video Annotation with Textual Descriptions", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.9, Issue 5, page no.332 - 344, May-2024, Available :http://www.ijrti.org/papers/IJRTI2405049.pdf |
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| ISSN: |
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2405049
Registration ID:189876
Published In: Volume 9 Issue 5, May-2024
DOI (Digital Object Identifier):
Page No: 332 - 344 Country: Bangalore , Karnataka, India Research Area: Information Technology Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2405049 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2405049 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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