Visual Search Engine for Optimized Video Segments
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Solution Overview
Problem
Users face difficulties in selecting video content due to overwhelming options, and existing trailers or clips often fail to engage users effectively, especially when they include distracting elements like logos or are in an unfamiliar language.
Innovation Solution
A visual search engine is used to generate optimized video segments by segmenting and localizing video content, adjusting start and end times, and removing overlays or language barriers, utilizing deep machine-learning models for feature extraction and similarity scoring to create engaging clips that match user preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If traditional trailers or clips are used to help users decide what to watch, then users can get a preview of content, but users spend more than two minutes and at least five webpage navigations to decide on streaming content
Solution Approach 1:
The video content is segmented into multiple clips of different durations (e.g., 15 seconds, 30 seconds, 60 seconds) and styles (e.g., highlight reels, scene-by-scene breakdowns). This segmentation allows users to quickly scan through multiple short clips to make a decision, rather than watching a single long trailer, thereby reducing the time and effort required to decide what to watch.
Solution Approach 2:
The system dynamically generates and serves different video clips based on user behavior, device type, and contextual information. Clips are adaptively selected and customized in real-time to match user preferences and viewing conditions, making the content discovery process more efficient and engaging while reducing decision time.
2Reliability
If trailers or clips include logos or other overlay images to identify content sources, then branding is provided, but the logos or overlays become distracting images that reduce user engagement
Solution Approach 1:
The system extracts and removes distracting overlay elements such as logos, watermarks, and promotional text from video clips while preserving the essential content identification information. This extraction process creates cleaner, more engaging clips that maintain content attribution without the harmful distracting effects of traditional overlays.
Solution Approach 2:
Different quality standards are applied to different parts of the video frame. Critical content identification elements are preserved with high fidelity, while distracting overlay regions are selectively removed or blurred. This local quality differentiation maintains reliability for content identification while eliminating user distraction.
3Quantity of substance
If the originally available trailer or clip is provided in a language the user does not speak, then the full content is available, but the user cannot understand or engage with the content
Solution Approach 1:
The system introduces automated translation and subtitle generation as intermediary processes between the original video content and the user. Multiple language versions and subtitle tracks are generated and made available, allowing users to comprehend content in their preferred language while the full original content remains accessible for those who speak the source language.
Data Source
AI summary
Systems and methods are provided herein for generating optimized video segments. A derivative video segment (e.g., a scene) can be identified from derivative video content (e.g., a movie trailer). The segment may be used a query to search video content (e.g., the movie) for the segment. Once found, an optimized video segment may be generated from the video content. The optimized video segment may have a different start time and/or end time than those corresponding to the original segment. Once optimized, the video segment may be presented to a user or stored for subsequent content recommendations.