Grouped-Image Retrieval with Maximum Embedding Extraction
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Solution Overview
Problem
Current image retrieval systems struggle to identify entities represented by collections of images due to challenges such as variable dimensionality, loss of information, and sensitivity to noise when summing or averaging embeddings, particularly in applications like e-commerce and social media platforms.
Innovation Solution
A system utilizing a trained machine learning model, like CLIP, generates embeddings for groups of images and compares them to identify similar entities, employing a late-interaction architecture for efficient processing and a reranking process to enhance accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If embeddings are summed or averaged to represent image collections, then retrieval can be performed on grouped images, but information loss and sensitivity to noise occur
Solution Approach 1:
The patent extracts and utilizes only the maximum embedding values from each image collection rather than aggregating all embeddings through summation or averaging. This extraction approach preserves critical information about the most representative images while avoiding the information loss inherent in mean/sum aggregation methods.
Solution Approach 2:
The patent changes the aggregation parameter from additive operations (sum/average) to a selection operation (maximum). This parameter change fundamentally alters how collection embeddings are computed, transforming the process from sensitive to noise and information-loss prone into a more robust method that captures the essence of the collection through its most representative elements.
2Adaptability or versatility
If embeddings are aggregated to represent image collections, then entity retrieval is enabled, but variable or increased dimensionality results
Solution Approach 1:
By extracting only the maximum embedding values from each collection, the patent reduces the effective dimensionality while maintaining retrieval capability. Instead of working with aggregated vectors that combine dimensions from multiple images, the system uses selected representative embeddings that preserve essential information with fewer dimensions.
3Productivity
If single image input data is used for retrieval, then computational efficiency is maintained, but inability to search for entities represented by collections of images occurs
Solution Approach 1:
The patent performs preliminary action by pre-computing and storing collection embeddings using the maximum embedding approach before retrieval operations. This allows the system to handle collection-based queries efficiently without requiring real-time aggregation of multiple images during the retrieval process, thus maintaining computational efficiency while enabling collection search capability.
Data Source
AI summary
Systems and methods for image retrieval are disclosed. In an example, sets of catalog images are received, wherein each set of catalog images is associated with a catalog item of a plurality of catalog items. Respective catalog embeddings representing each set of catalog images are generated. Query images associated with a query item are received. Query embeddings representing the query images are generated. Based on comparisons of the query images and the catalog images, select a candidate set of catalog items from the plurality of catalog items. Based on a comparison of the query embeddings and respective catalog embeddings associated with respective catalog items of the candidate set, generate respective similarity scores. Based on the similarity scores, determine that the query item is similar to a candidate catalog item, and in response identify the query item for review.