Visual Access Code for Media Object Indexing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional media object indexing techniques are limited, with manual reclassification required for changes in classification types, keyword-based systems being cumbersome and error-prone, and fully-automated systems lacking accuracy in object recognition and relationship determination.
Innovation Solution
A visual access code system that uses images and hotspots to generate a unique authentication code, allowing for advanced searching and browsing capabilities by associating semantic information with media objects based on attributes, relationships, and classifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual reclassification is used to change classification types, then classification accuracy is maintained, but user effort and time consumption increase significantly
Solution Approach 1:
The system automatically performs reclassification by detecting semantic relationships between media objects and classification labels, eliminating the need for manual user intervention. The computer vision model autonomously analyzes image content and reassigns classifications based on detected objects, scenes, and attributes.
Solution Approach 2:
Manual classification operations are replaced with an automated computer vision-based classification system. The mechanical process of manual tagging is substituted with automated image analysis and semantic understanding algorithms that can rapidly process and reclassify large volumes of media objects.
2Adaptability or versatility
If keyword-based classification is used, then dynamic albums can be created, but system complexity and error-proneness increase due to manual tag input
Solution Approach 1:
The system automatically generates classification tags by analyzing image content through computer vision models. Instead of requiring users to manually input keywords, the system self-generates relevant tags based on detected objects, scenes, and semantic relationships within the media objects.
Solution Approach 2:
The system extracts and copies semantic information from image content to generate classification tags. By analyzing visual features and semantic relationships in images, the system creates accurate tags that reflect the actual content without requiring manual user input.
3Adaptability or versatility
If direct attribute classification is used, then dynamic albums based on attributes can be created, but user interface quality and search capability are limited
Solution Approach 1:
The system transitions from simple direct attribute classification to multi-dimensional semantic classification. By incorporating scene detection, object recognition, and relationship analysis, the system creates rich semantic tags that enable more sophisticated searching and browsing capabilities across multiple dimensions of media content.
Solution Approach 2:
The classification system combines multiple types of information including detected objects, scene context, temporal relationships, and spatial relationships to create composite semantic tags. This composite approach enriches the classification data structure and enables more powerful search and filtering operations.
4Productivity
If automated image recognition is used, then indexing speed is improved, but accuracy in object identification and relationship determination decreases
Solution Approach 1:
The automated classification process is divided into multiple specialized stages: object detection, scene recognition, relationship analysis, and semantic tag generation. Each stage focuses on a specific aspect of image understanding, allowing the system to maintain high speed while improving accuracy through specialized processing at each step.
Solution Approach 2:
The system incorporates feedback mechanisms where classification results are continuously refined based on detected semantic relationships. The model learns from the interconnections between detected objects and their relationships to the media object, improving accuracy while maintaining automated processing speed.
Data Source
AI summary
A method for registering and authenticating a user based on a visual access code. The method includes presenting, to the user, images; receiving a selection of a first image; receiving a selection of at least a first set of hotspots from a plurality of hotspots included in the first image; and generating a visual access code based at least in part on the selection of the first image and the first set of hotspots.