Image Retrieval Device with Learning Region Feedback
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Solution Overview
Problem
Conventional image retrieval systems using textual search queries face challenges due to mismatches in user-defined and system-defined color ranges, scales, and measurements, leading to inefficient trial-and-error processes and unclear causes of retrieval failures, with users unable to adjust the range of retrievable images effectively.
Innovation Solution
A retrieval device that analyzes image regions, converts textual search queries into attributes, retrieves and displays image regions based on similarity scores, and provides learning image regions for reference, allowing users to evaluate retrieval accuracy and adjust search queries or recognition errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If textual search queries are used for image retrieval, then users can easily handle the search process, but the range of expression conceived by users may not match the range conceived by system designers, resulting in unintended search results
Solution Approach 1:
The system displays learning images (actual training data) alongside search results, providing feedback to users about what the system actually recognizes. This allows users to understand the system's interpretation of their search query and adjust their expectations or queries accordingly, resolving the mismatch between user intent and system interpretation.
Solution Approach 2:
Learning images serve as an intermediary between the user's textual search query and the system's internal recognition criteria. By showing users actual images used to train the system, they can bridge the gap between their mental model of the search and the system's actual behavior.
2Reliability
If users repeat trial and error for the right text representation of the search query, then they can find the intended image, but the retrieval process becomes inefficient and time-consuming
Solution Approach 1:
The system performs preliminary action by displaying learning images before the user finalizes their search. This allows users to see what the system will actually retrieve with their query, enabling them to adjust their search terms in advance rather than through repeated trial and error, thus saving time while maintaining reliability.
Solution Approach 2:
By providing immediate feedback through learning image display, users can understand the system's interpretation and refine their search query in real-time, reducing the number of trial-and-error iterations needed to achieve accurate retrieval.
3Reliability
If users visually check all of a large number of candidates within the range, then they can ensure complete coverage, but the process becomes inefficient
Solution Approach 1:
The system extracts and displays representative learning images that exemplify the search criteria. Instead of requiring users to check all candidates, the system extracts key examples that demonstrate what the search will return, allowing users to efficiently assess whether the search is appropriate without manual verification of every result.
4Device complexity
If the system uses fixed range of blue colors preset by system design, then the retrieval process is simplified, but users cannot know information on the system design and cannot adjust the range of retrievable images
Solution Approach 1:
The system provides feedback by displaying learning images that show the actual range of colors and attributes the system recognizes. Users can see the fixed range the system uses and understand its limitations, enabling them to work within or around these constraints while maintaining system simplicity.
Solution Approach 2:
Learning images act as an intermediary that translates the system's internal fixed ranges into visible, understandable examples for users. This allows users to comprehend the system's preset ranges without exposing the underlying complexity, while still enabling adaptive search strategies.
Data Source
AI summary
According to one embodiment, a retrieval device includes one or more processors configured to retrieve a plurality of search image regions from an intended image through image search using a search query, extract a plurality of learning image regions from a learning image used in learning of the image search, through the image search using the search query, and display the search image regions and the learning image regions on a display.