Dynamic Image Search Through Selectable Query Objects
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Solution Overview
Problem
Conventional image search techniques fail to accommodate user interest in specific objects within an image, leading to irrelevant search results when users manually crop or modify images, especially when objects are obscured or require rearrangement.
Innovation Solution
Implement automated object detection and selection, allowing users to choose specific objects for query images, and optionally rearrange them to better match search intent, using machine learning models for accurate image retrieval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional image search uses the entirety of the uploaded query image, then the search engine can process the query without additional user interaction, but the search results include objects that the user may not be interested in, reducing search relevance
Solution Approach 1:
The system segments the query image into multiple detected objects and allows the user to select only the specific objects of interest. This segmentation enables precise control over which objects are included in the search, improving search relevance while maintaining ease of operation through automated object detection and selection interface.
Solution Approach 2:
The system extracts specific objects from the query image that the user is interested in, separating them from other objects in the image. This extraction process allows the search to focus only on the selected objects, eliminating irrelevant content and improving search precision without requiring manual image editing.
2Measurement precision
If users manually crop or modify images to focus on specific objects, then search results can be more relevant, but the process becomes complex and time-consuming, especially when objects are obscured or require rearrangement
Solution Approach 1:
The system performs automated object detection, segmentation, and arrangement itself, eliminating the need for users to manually crop or edit images. The automated processes handle complex tasks such as identifying obscured objects and arranging selected objects, reducing the complexity of the user's workflow while maintaining high search relevance.
Solution Approach 2:
The system performs preliminary object detection and segmentation before the user submits the search query. By pre-processing the image to identify and separate objects, the system prepares the data in advance, reducing the complexity of subsequent user actions and enabling faster, more accurate search operations.
3Measurement precision
If the system automatically detects and allows selection of specific objects, then search precision is improved, but the system complexity increases due to object detection and selection mechanisms
Solution Approach 1:
The system introduces an intermediary object selection interface that mediates between the automated object detection process and the final search query. This intermediary layer presents detected objects to the user in a simplified manner, allowing easy selection while managing the complexity of the underlying detection algorithms and processing mechanisms.
Data Source
AI summary
This document relates to searching for images using a query image. For example, an input image can be received and analyzed to identify objects, and then identification of two or more selected objects can be received. A query image can be derived from the input image, where the query image includes the selected objects. The query image can be used to perform a search to identify related images, which can be output in response to the query image.