SEARCHING FOR IMAGES USING GENERATED IMAGES

Smart overview of the Invention

A system is designed to enhance image searching capabilities by using generated images. This system receives a natural language search query from a user and processes it using a machine learning model trained to interpret such inputs. The model generates digital images based on the query, which are then used to perform an image-based search within a vast digital image repository. The results are displayed in a user interface, providing users with relevant images that match the semantic intent of their search.

Background

Traditional methods for searching digital image repositories rely on either natural language or image-based searches. These methods often fall short as natural language searches may not capture the true intent behind a user's query, while image-based searches require an input image that might not be available. The described system addresses these limitations by generating images from text prompts, thereby bridging the gap between user intent and search results.

Machine Learning Models

The system employs two machine learning models: a generative model and a natural language model. The natural language model processes the user's text query to create prompts for the generative model, which then produces digital images. This approach allows for capturing diverse visual features that align with the semantic intent of the search query, offering a significant improvement over conventional search methods.

Search and Display Process

Once the generated images are created, they are used to conduct an image-based search within the repository. The resulting images are grouped into clusters based on perceptual similarities. These clusters help organize the display of results in the user interface, prioritizing images that match specific characteristics of the user's digital content, such as aspect ratios or color distributions.

Advantages and Implementation

By integrating machine learning models to generate and utilize images for searching, the system provides higher quality and more relevant search results compared to traditional methods. It enhances user experience by aligning search outcomes with the semantic intent of queries. This innovative approach is adaptable across various devices, from powerful desktops to mobile devices, ensuring broad applicability and accessibility.