Search-Driven Image Editing With Multi-Modal Image Retrieval
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Solution Overview
Problem
Conventional image editing systems are inflexible and inefficient, requiring significant user interaction and limiting search inputs to a single type, failing to provide their own features for retrieving reference images, and lacking control over how separate input types are used in image searches.
Innovation Solution
A search-based editing system that utilizes flexible search engines capable of multi-modal search inputs and neural networks to retrieve and modify digital images, incorporating a graphical user interface that consolidates search and editing options, reducing the need for user interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional image editing systems use single-type search inputs, then the system structure remains simple, but the search flexibility and editing capability are limited
Solution Approach 1:
The system integrates multiple search engine types (image search engine and text search engine) into a unified image editing system. The image search engine accepts image inputs to find reference images, while the text search engine processes text queries to retrieve relevant images. Both search engines feed into the same image editing module, enabling the system to handle diverse search inputs (image and text) through a single multi-functional platform, thereby improving search flexibility without proportionally increasing system complexity.
2Productivity
If the system requires significant user interaction for image editing, then the editing precision can be maintained, but the productivity and efficiency are reduced
Solution Approach 1:
The system automatically retrieves reference images from search engines based on user input (image or text query) without requiring manual selection. The image editing module automatically applies attributes from retrieved reference images to modify the input image. This automated reference image retrieval and attribute transfer process reduces the need for extensive user interaction while maintaining editing quality, thereby improving productivity and editing efficiency.
3Ease of operation
If the system integrates search and editing functions, then the ease of operation is improved, but the device complexity increases
Solution Approach 1:
The system merges the image search engine, text search engine, and image editing module into a single integrated platform. Users can input either an image or text query, and the system automatically routes the input to the appropriate search engine, retrieves reference images, and applies editing operations in one seamless workflow. This consolidation allows users to access both search and editing functions through a single interface, improving ease of operation while managing system complexity through unified architecture.
4Measurement precision
If the system uses multiple search engines for multi-modal searches, then the measurement precision of search results is improved, but the loss of time for processing increases
Solution Approach 1:
The system pre-processes and embeds image data into feature vectors using the image search engine before actual search queries are executed. When a search query arrives (whether image or text-based), the system can quickly compare queries against pre-processed reference data and retrieve relevant images faster. This preliminary embedding and indexing of image data enables multi-modal searches across multiple engines without proportionally increasing processing time, maintaining search result accuracy while reducing query response time.
Data Source
AI summary
Systems, methods, and non-transitory computer-readable media implements related image search and image modification processes using various search engines and a consolidated graphical user interface. For instance, one or more embodiments involve receiving an input digital image and search input and further modify the input digital image using the image search results retrieved in response to the search input. In some cases, the search input includes a multi-modal search input having multiple queries (e.g., an image query and a text query), and one or more embodiments involve retrieving the image search results utilizing a weighted combination of the queries. Some implementations involve generating an input embedding for the search input (e.g., the multi-modal search input) and retrieving the image search results using the input embedding.