Interactive Concept Learning Image Search
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current image search technologies rely heavily on metadata and keywords, which are impractical for large databases and insufficient for capturing the visual properties of images, leading to inefficient image retrieval, especially for images generated automatically or with complex characteristics.
Innovation Solution
The interactive concept learning image search technique allows users to create rules based on visual and semantic features, enabling the ranking or re-ranking of images by learning common characteristics from example images, independent of metadata, using machine learning methods like Support Vector Machines and nearest-neighbor classifiers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If keyword-based metadata search is used, then image retrieval can be conducted with simple query mechanisms, but the system cannot accurately capture visual properties and is impractical for very large databases
Solution Approach 1:
The patent introduces an intermediary component that automatically generates visual tags from image content analysis. This intermediary layer bridges the gap between simple keyword queries and accurate visual property matching, allowing users to search with keywords while the system automatically translates these into visual feature comparisons for precise retrieval
Solution Approach 2:
The system implements self-service by automatically generating and updating visual tags for images without requiring manual metadata annotation. The image search system autonomously analyzes image content, extracts visual features, and maintains its own indexing structure, eliminating the need for impractical human labeling of large databases
2Productivity
If a handful of automatic meta tags are generated, then image search can be performed without manual labeling, but the tags are insufficient for characterizing complex visual properties and it is unclear what tags users want
Solution Approach 1:
The patent implements dynamic tag generation where the system adaptively creates and refines visual tags based on user feedback and search patterns. Rather than using a static set of predefined tags, the system dynamically adjusts which visual features are extracted and emphasized, allowing it to capture complex visual properties while remaining responsive to user needs
Solution Approach 2:
The system transitions from traditional single-dimension keyword tags to multi-dimensional visual feature tags. It extracts and utilizes multiple visual dimensions simultaneously (color, texture, shape, spatial relationships), creating a richer tag structure that comprehensively characterizes complex visual properties while maintaining automatic generation efficiency
3Device complexity
If traditional keyword matching is used, then the search system has simple structure and fast execution, but it cannot retrieve images based on visual characteristics like vertical lines or complex patterns
Solution Approach 1:
The patent creates a universal image search system that handles multiple search modes through a single integrated architecture. The system can process both traditional keyword queries and visual feature-based queries using the same infrastructure, making it versatile for different types of image retrieval while maintaining structural efficiency through shared components for feature extraction and matching
Data Source
Figure 1
Figure 2
Figure 3
AI summary
An interactive concept learning image search technique that allows end-users to quickly create their own rules for re-ranking images based on the image characteristics of the images. The image characteristics can include visual characteristics as well as semantic features or characteristics, or may include a combination of both. End-users can then rank or re-rank any current or future image search results according to their rule or rules. End-users provide examples of images each rule should match and examples of images the rule should reject. The technique learns the common image characteristics of the examples, and any current or future image search results can then be ranked or re-ranked according to the learned rules.