Image Embedding Function for Object Grouping
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Solution Overview
Problem
Manual image organization is time-consuming due to the volume and complexity of stored images, and existing automated methods struggle with grouping images where facial recognition is ineffective, such as when faces are not recognizable due to size, occlusion, or angle.
Innovation Solution
Training an image embedding function that generates similar feature representations for images depicting the same instance of an object within a specified duration and dissimilar representations for different instances, using image triplets or pairs to iteratively adjust parameter weights, allowing for effective image grouping without relying on facial recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If facial recognition process is used to group images, then images with recognizable faces can be effectively organized, but images with unrecognizable faces (too small, occluded, or at angle) cannot be processed
Solution Approach 1:
The patent changes the parameters used for image comparison from face-specific features to general object features. The embedding function transforms images into feature vectors that capture overall object characteristics rather than relying on facial recognition parameters, enabling the system to handle images where faces are unrecognizable.
Solution Approach 2:
The patent segments the image processing task into feature extraction and similarity comparison components. By using an embedding function that extracts general object features rather than relying on facial recognition, the system can process images regardless of face recognizability, effectively segmenting the problem from face-specific constraints.
2Manufacturing precision
If manual viewing and grouping of images is performed, then accurate organization can be achieved, but the process becomes time-consuming due to volume and complexity of stored images
Solution Approach 1:
The patent replaces the mechanical process of manual image viewing and grouping with an automated computational system. The embedding function and similarity metric automatically compute image relationships, substituting human manual operations with algorithmic processing that is both accurate and efficient for large volumes of images.
Solution Approach 2:
The patent creates feature vector copies of images that capture essential characteristics. Instead of manually viewing and comparing original images, the system works with compressed feature representations that preserve the information needed for accurate grouping while enabling rapid automated processing.
3Speed
If traditional image similarity methods are used, then processing speed may be maintained, but the quality of feature representation is insufficient for robust image organization
Solution Approach 1:
The patent transforms images from pixel space into a higher-dimensional feature vector space using the embedding function. This dimensional transformation allows the system to capture complex image characteristics that are not apparent in the original image space, improving feature representation quality while maintaining processing efficiency through the learned embedding.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for object detection and representation in images. In one aspect, a method includes detecting occurrences of objects of a particular type in images captured within a first duration of time, and iteratively training an image embedding function to produce as output representations of features of the input images depicting occurrences of objects of the particular type, where similar representations of features are generated for images that depict the same instance of an object of a particular type captured within a specified duration of time, and dissimilar representations of features are generated for images that depict different instances of objects of the particular type.