Joint Embedding Space for Scalable Semantic Item Association
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Solution Overview
Problem
Conventional methods for associating items of various types, such as images, audio, and video, based on semantic relationships are inefficient and do not scale well with large data sets, leading to inadequate determination of semantic relationships in very large collections of items.
Innovation Solution
The method involves embedding training items of multiple types into a joint embedding space, learning mappings for each type, and associating new items based on distance in this space, allowing for scalable and efficient determination of semantic relationships without requiring manually annotated images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional methods use independent classifiers for each image category, then classification can be performed for each category, but the system becomes inefficient and performance degrades with large data sets and numerous annotations
Solution Approach 1:
The patent combines multiple independent classifiers into a single joint classifier that processes all image categories simultaneously. Instead of training separate SVMs for each category, the system uses a unified probabilistic framework that handles multiple categories in one model, reducing computational overhead and improving efficiency with large data sets
Solution Approach 2:
The joint classifier serves multiple functions by handling classification across all image categories within a single model. The system universally processes different category types (e.g., animal, vehicle, object) using the same probabilistic framework, eliminating the need for category-specific classifiers
2Measurement precision
If conventional methods use K-nearest neighbor methods with large training data sets, then annotation accuracy can be maintained, but finding nearest neighbors becomes highly inefficient
Solution Approach 1:
The system changes the approach from distance-based nearest neighbor search to probability-based classification. Instead of computing distances to find K-nearest neighbors in high-dimensional space, the joint classifier directly computes posterior probabilities for each category, which is computationally more efficient and scales better with large data sets while maintaining annotation accuracy
3Extent of automation
If conventional methods cluster pre-annotated training images and determine annotations based on cluster similarity, then annotation can be performed without manual labeling, but the method does not scale to very large data sets
Solution Approach 1:
The system transitions from clustering-based annotation to probabilistic classification. Instead of relying on cluster assignments and similarity metrics, the joint classifier directly computes the probability of each category given the image features, providing a more scalable approach that handles very large data sets efficiently while maintaining automatic annotation capability
4Reliability
If conventional probabilistic approaches are used with large data sets, then relationships can be modeled, but re-calibration is required when probability of one relationship changes, making the system highly inefficient
Solution Approach 1:
The joint classifier incorporates feedback mechanisms where the classification of one category automatically updates the probabilities for other categories. When new training data or annotations are added, the system re-calibrates all category probabilities simultaneously through the unified probabilistic framework, eliminating the need for iterative re-calibration and improving efficiency
Data Source
AI summary
Methods and systems to associate semantically-related items of a plurality of item types using a joint embedding space are disclosed. The disclosed methods and systems are scalable to large, web-scale training data sets. According to an embodiment, a method for associating semantically-related items of a plurality of item types includes embedding training items of a plurality of item types in a joint embedding space configured in a memory coupled to at least one processor, learning one or more mappings into the joint embedding space for each of the item types to create a trained joint embedding space and one or more learned mappings, and associating one or more embedded training items with a first item based upon a distance in the trained joint embedding space from the first item to each said associated embedded training items. Exemplary item types that may be embedded in the joint embedding space include images, annotations, audio and video.