Integrating YOLO and WordNet for automated image object summarization
The demand for methods that automatically create text summaries from images containing many things has recently grown. Our research introduces a fresh and creative way to achieve this. We bring together the WordNet dictionary and the YOLO model to make this happen. YOLO helps us find where the thing...
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| Veröffentlicht in: | Signal, image and video processing image and video processing, 2024-12, Vol.18 (12), p.9465-9481 |
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| container_title | Signal, image and video processing |
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| creator | Saqib, Sheikh Muhammad Aftab, Aamir Mazhar, Tehseen Iqbal, Muhammad Shahazad, Tariq Almogren, Ahmad Hamam, Habib |
| description | The demand for methods that automatically create text summaries from images containing many things has recently grown. Our research introduces a fresh and creative way to achieve this. We bring together the WordNet dictionary and the YOLO model to make this happen. YOLO helps us find where the things are in the images, while WordNet provides their meanings. Our process then crafts a summary for each object found. This new technique can have a big impact on computer vision and natural language processing. It can make understanding complicated images, filled with lots of things, much simpler. To test our approach, we used 1381 pictures from the Google Image search engine. Our results showed high accuracy, with 72% for object detection. The precision was 85%, the recall was 72%, and the F1-score was 74%. |
| doi_str_mv | 10.1007/s11760-024-03560-z |
| format | Article |
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| source | Springer Nature - Complete Springer Journals |
| subjects | Computer Imaging Computer Science Computer vision Image Processing and Computer Vision Multimedia Information Systems Natural language processing Object recognition Original Paper Pattern Recognition and Graphics Search engines Signal,Image and Speech Processing Vision |
| title | Integrating YOLO and WordNet for automated image object summarization |
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