Color Attribute Prediction via Attention Network Segmentation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional color classification systems fail to accurately detect colors in images as perceived by humans, leading to inconsistent search results due to factors like background colors, neighboring colors, and semantic context, which can result in images not matching user expectations during color-based searches.
Innovation Solution
A color classification system using a machine learning model with an attention network that computes object features, generates attention vectors based on query and key-value pairs for colors, and produces color attribute vectors indicating the probability of colors in an image, aligning with human perception by incorporating object types and semantic content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional color classification systems are used, then color detection is performed, but the detected colors do not match human perception due to background colors, neighboring colors, and semantic context
Solution Approach 1:
The system segments the image into multiple object regions using object detection, then processes each region separately to identify colors specific to each object. This segmentation isolates object colors from background and neighboring colors, resolving the contradiction between color detection and semantic context preservation.
Solution Approach 2:
The system introduces an intermediary attention mechanism that weighs and selects relevant color information from different regions. This attention mechanism acts as a mediator between raw pixel data and final color classification, filtering out irrelevant background and neighboring colors while preserving semantically relevant object colors.
2Measurement precision
If color classification is performed on entire images, then color information is obtained, but background and neighboring colors interfere with accurate object color identification
Solution Approach 1:
The system divides the image into multiple segmented regions corresponding to different detected objects. By processing each segmented region independently, the system eliminates interference from background and neighboring colors, achieving accurate object color identification.
Solution Approach 2:
The system applies different processing focus to different regions of the image. Each object region is analyzed with attention to its specific color characteristics, while background and non-relevant regions are excluded from color classification. This local quality approach ensures that color identification is performed only on relevant object areas.
Data Source
AI summary
The present disclosure describes a model for large scale color prediction of objects identified in images. Embodiments of the present disclosure include an object detection network, an attention network, and a color classification network. The object detection network generates object features for an object in an image and may include a convolutional neural network (CNN), region proposal network, or a ResNet. The attention network generates an attention vector for the object based on the object features, wherein the attention network takes a query vector based on the object features, and a plurality of key vector and a plurality of value vectors corresponding to a plurality of colors as input. The color classification network generates a color attribute vector based on the attention vector, wherein the color attribute vector indicates a probability of the object including each of the plurality of colors.