Image Selection Indexing With Embeddings for Faster Semantic Search
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current machine learning techniques are inefficient and inaccurate when searching through large pools of images, such as millions, to identify relevant images within a short time frame, and generic models like CLIP fail to align image content with textual meaning.
Innovation Solution
Utilizing multiple machine learning models to build a context-specific index, identify keywords, and compare embedded versions of keywords and images, employing a nearest neighbor algorithm to reduce data searched and enhance processing speed and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple machine learning models are used to build a context-specific index and compare embedded versions of keywords and images, then measurement precision and reliability are improved, but device complexity increases
Solution Approach 1:
The system segments the image search task into multiple independent processing stages: text embedding, image embedding, index construction, and similarity comparison. Each stage is handled by specialized machine learning models that process specific aspects of the data, allowing for higher overall accuracy while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The patent introduces embedded vectors as intermediary representations between raw text/images and the comparison operation. Multiple machine learning models transform input data into these intermediate embedding spaces, where semantic relationships can be efficiently computed through similarity metrics, thereby improving precision without directly increasing the complexity of the final comparison operation.
2Productivity
If a nearest neighbor algorithm is used to reduce data searched, then productivity is improved, but measurement precision may be affected
Solution Approach 1:
The system performs preliminary action by pre-computing and storing embedded representations of all images in the database during an indexing phase. This allows the nearest neighbor algorithm to work with compact pre-processed data rather than raw images, dramatically improving processing speed while maintaining precision through the use of high-quality pre-computed embeddings.
Solution Approach 2:
The patent transforms the search problem by changing the parameter space from raw pixel data to embedded vector representations. This parameter transformation enables the nearest neighbor algorithm to operate efficiently on reduced-dimensional data while preserving semantic information, thus achieving both speed improvement and precision maintenance.
Data Source
AI summary
A method including receiving an input including a number of texts from a source of text and a number of images from a source of images. The texts are separate from the images. The input is embedded into a first data structure that defines first relationships among the images from the source of images and the texts from the source of text. The first data structure is compared to an index including a second data structure that defines second relationships among a number of pre-determined texts and a number of pre-determined images. The pre-determined texts have known relationships to the pre-determined images. Each pre-determined image in the pre-determined images is related to one or more instances of the pre-determined texts. A subset of images, those images in the pre-determined images for which matches exist between the first relationships and the second relationships, is returned from the pre-determined images.