Body Type Classification via Embedding Vectors
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Solution Overview
Problem
The vast amount of accessible content on the internet makes it difficult for users to find and access relevant content, as identifying proper keywords or queries and browsing through search results can be time-consuming and challenging.
Innovation Solution
The system determines one or more body types of individuals represented in content items, such as images, by using multi-dimensional embedding vectors that encode features of each content item. This allows for the association of body types with content items, facilitating indexing, filtering, and recommendation without the need for image pre-processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users manually identify keywords or queries to search for relevant content, then they can find specific content, but the process becomes time-consuming and difficult
Solution Approach 1:
The system automatically performs body type classification on content items without requiring user input or manual keyword identification. The machine learning model autonomously processes images and videos, generating body type labels that enable automatic indexing and filtering, allowing the system to serve itself rather than relying on user effort
Solution Approach 2:
The system pre-processes and classifies content items by body type in advance, before users need to search for them. By automatically generating body type labels for all content items in the corpus during indexing, the system prepares the data structure ahead of time, enabling rapid filtering and retrieval when users do search
2Measurement precision
If users browse through search results to identify relevant content, then they can find appropriate content, but the process is time-consuming and difficult
Solution Approach 1:
The system extracts and highlights content items that match the user's desired body type criteria directly in the search results. By filtering the content corpus based on body type labels and presenting only matching results, the system removes irrelevant content from the browsing process, allowing users to quickly identify relevant content without manually scanning through unrelated items
3Measurement precision
If the system performs image pre-processing to determine body types, then it can accurately classify individuals, but the process becomes more complex
Solution Approach 1:
The system uses a universal embedding vector representation that can be generated from various image preprocessing methods (or none at all). The same machine learning model handles both cases where images are pre-processed and where they are not, making the system multi-functional and adaptable without requiring separate processing pipelines for different accuracy requirements
Data Source
AI summary
Described are systems and methods to determine body types of individuals represented in content items. The determined body types may be associated with the content items to facilitate indexing, filtering, diversifying, etc. of the content items based on the determined body types. In exemplary implementations, a corpus of content items may be associated with an embedding vector that includes a representation of the content item. The embedding vectors associated with each content item can be provided as inputs to a trained machine learning model, which can process the embedding vectors to determine one or more body types of individuals represented in each content item while eliminating the need for performing image pre-processing prior to determination of the body type(s) presented in the content item.