18510354. VIDEO SUMMARIZATION USING SEMANTIC INFORMATION simplified abstract (Intel Corporation)
Contents
- 1 VIDEO SUMMARIZATION USING SEMANTIC INFORMATION
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 VIDEO SUMMARIZATION USING SEMANTIC INFORMATION - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
VIDEO SUMMARIZATION USING SEMANTIC INFORMATION
Organization Name
Inventor(s)
Myung Hwangbo of Lake Oswego OR (US)
Krishna Kumar Singh of Davis CA (US)
Teahyung Lee of Chandler AZ (US)
Omesh Tickoo of Portland OR (US)
VIDEO SUMMARIZATION USING SEMANTIC INFORMATION - A simplified explanation of the abstract
This abstract first appeared for US patent application 18510354 titled 'VIDEO SUMMARIZATION USING SEMANTIC INFORMATION
Simplified Explanation
The patent application describes an apparatus that uses a machine learning algorithm to process images and determine scores for video segments based on the detected actions associated with the images.
- The apparatus processes a first image of a video segment with a machine learning algorithm to determine a score.
- The machine learning algorithm detects actions associated with images, which are labeled.
- The apparatus determines a second score for the video segment based on the scores of corresponding images.
- Based on the second score, the apparatus decides whether to retain the video segment in memory.
Potential Applications
This technology could be applied in video editing software to automatically detect and score different actions in video segments, making it easier for users to identify and select relevant content.
Problems Solved
This technology solves the problem of manually reviewing and scoring video content by automating the process through machine learning algorithms, saving time and effort for users.
Benefits
The benefits of this technology include increased efficiency in video editing tasks, improved organization of video content, and enhanced user experience through automated action detection and scoring.
Potential Commercial Applications
A potential commercial application of this technology could be in the development of video editing software for professionals and content creators, offering advanced features for analyzing and selecting video segments based on detected actions.
Possible Prior Art
One possible prior art for this technology could be existing video analysis software that uses machine learning algorithms to detect and classify objects or actions in videos. However, the specific application of determining scores for video segments based on detected actions may be a novel aspect of this patent application.
Unanswered Questions
How does the apparatus handle variations in lighting conditions when processing images for action detection?
The patent application does not provide details on how the apparatus adjusts for different lighting conditions that may affect the accuracy of action detection in images.
What is the computational complexity of the machine learning algorithm used in the apparatus?
The patent application does not mention the computational complexity of the machine learning algorithm, which could impact the processing speed and efficiency of the apparatus.
Original Abstract Submitted
Example apparatus disclosed herein are to process a first image of a first video segment from the image capture sensor with a machine learning algorithm to determine a first score for the first image, the machine learning algorithm to detect actions associated with images, the actions associated with labels. Disclosed example apparatus are also to determine a second score for the first video segment based on respective first scores for corresponding images in the first video segment. Disclosed example apparatus are further to determine, based on the second score, whether to retain the first video segment in the memory.
