Color-Based Outfit Classification Using Semantic Embeddings
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Solution Overview
Problem
Existing color-based outfit classification methods suffer from inconsistent accuracy due to the quality of training datasets, input image quality, and similarities in colors, particularly in sports broadcasts where players may appear small and uniforms can resemble each other.
Innovation Solution
A method using a color-based neural network that involves training with a dataset of images, generating semantic embeddings, and employing a triplet loss cost function to minimize misclassifications by distinguishing between similar colors based on metadata and mask inputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If color-based classification is used for speed, then processing time is reduced, but classification accuracy becomes inconsistent
Solution Approach 1:
The system transforms color classification from direct pixel comparison to semantic embedding space comparison. By changing the parameter space from raw color values to learned semantic representations, the system achieves both speed (through efficient embedding comparison) and accuracy (through nuanced semantic differentiation of similar colors)
Solution Approach 2:
Semantic embeddings serve as an intermediary between raw color data and classification decisions. The embedding layer translates color inputs into a intermediate representation space where similarities are more discriminative, allowing accurate distinction between similar colors while maintaining processing efficiency
2Measurement precision
If training dataset size is increased to cover all outfits, then classification accuracy improves, but data processing complexity increases
Solution Approach 1:
The system changes the parameter representation from discrete color categories to continuous semantic embeddings. This transformation allows the model to generalize from fewer training examples by learning continuous color spaces, reducing the need for exhaustive training datasets while maintaining high accuracy
Solution Approach 2:
The semantic embedding space provides a dynamic, continuous representation of color that adapts to different lighting and viewing conditions. This dynamic representation allows the system to handle variability in training data more effectively, reducing the need for overly large datasets to cover all possible variations
3Measurement precision
If similar colors are distinguished more accurately, then misclassification rate decreases, but computational requirements increase
Solution Approach 1:
The system performs preliminary color normalization and embedding transformation before classification. By pre-processing colors into semantic embeddings that inherently separate similar hues, the system reduces the computational burden during actual classification while improving distinction accuracy between similar colors
Data Source
AI summary
Disclosed herein are systems and method for classifying objects in an image using a color-based neural network. A method may include: training a neural network to classify an object in a given image into a color class from a set of color classes; determining, from the set of color classes, a subset of color classes that are anticipated to be in a received input image based on image metadata; generating a matched mask input indicating the subset; inputting both the input image and the matched mask input into the neural network, wherein the neural network is configured to: determine a first semantic embedding of the input image and the matched mask input; outputting a color class associated with a second semantic embedding with a least amount of distance to the first semantic embedding from a plurality of semantic embeddings.