Personalized Image Cropping via User Profile Metadata
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Solution Overview
Problem
Current media guidance applications do not personalize images associated with media assets based on user preferences, leading to a lack of relevance in content display.
Innovation Solution
A system and method that uses user profile information to identify and crop preferred entities from images corresponding to media assets, such as posters or box art, by employing image recognition algorithms and metadata cross-referencing to generate personalized displays.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If generic images are used for all users, then device complexity is reduced, but user engagement and content relevance deteriorate
Solution Approach 1:
The image is divided into multiple portions, each containing a different entity (actor, character, or object). Control circuitry identifies and segments the image based on entity boundaries, allowing selective display of relevant portions based on user profile preferences without processing entire images for all users.
Solution Approach 2:
Control circuitry extracts and displays only the preferred entity portions from the original image based on user profile preferences. This extraction process removes irrelevant portions while maintaining the original image quality for targeted users, personalizing content without requiring complete image regeneration.
2Loss of information
If image cropping based on user profile is implemented, then content relevance improves, but processing time increases
Solution Approach 1:
User profiles containing entity preferences are pre-established and stored in the system before image display. When an image is presented, control circuitry quickly references the pre-existing profile to determine which entity portions to display, avoiding real-time preference analysis and reducing processing time.
Solution Approach 2:
The system uses metadata or pre-identified entity portions from the original image rather than performing complex real-time image analysis. This copying approach allows rapid retrieval and display of relevant image portions based on user preferences without extensive processing.
3Measurement precision
If entity identification algorithms are applied to all images, then personalization accuracy improves, but computational resources increase
Solution Approach 1:
The control circuitry employs a universal entity identification system that can recognize multiple types of entities (actors, characters, objects) using the same algorithmic approach. This multi-functional capability allows accurate identification across diverse image types without requiring separate specialized processing for each entity type, optimizing computational resource usage.
Solution Approach 2:
The system uses metadata associated with images to automatically identify entities without requiring extensive computational image analysis. This self-service approach leverages pre-existing information structures to achieve accurate entity identification while minimizing additional computational energy consumption.
Data Source
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AI summary
Systems and methods are provided herein for personalizing images that correspond to a media asset identifier by using user profile information. As an example, the television series "Community" has several actors, such as Joel McHale, Chevy Chase, and Ken Jeong, Poster art developed by an editor of "Community" may include an image that portrays each of Joel McHale, Chevy Chase, and Ken Jeong. In order to personalize the image, control circuitry may determine which actor(s) the user prefers, and crop out only those actors in the poster art to create a personalized image. As an example, if the user prefers Joel McHale, control circuitry may crop out the portrayal of Joel McHale and use only that portion of the image to display next to other text describing "Community."