Color Descriptor System for Image Tagging Accuracy
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Solution Overview
Problem
Existing electronic marketplace systems face challenges in efficiently tagging and filtering products by color due to the scalability issues of manual image tagging, leading to difficulties in refining search results based on visual attributes like color, which are crucial for user experience.
Innovation Solution
A method is introduced to automatically determine color information from product images using a color descriptor system that analyzes images in the HSV color space, segments colors based on human perception, and employs k-nearest neighbor voting to accurately assign colors, enabling effective 'query by color' and 'browse-by-color' functionalities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual tagging of each product image is performed, then color information can be accurately assigned to images, but the process is not scalable and requires excessive time and resources
Solution Approach 1:
The patent replaces the manual mechanical process of color tagging with an automated image processing system that uses color space transformation (RGB to HSV) and histogram analysis to automatically determine product colors. This substitution eliminates the need for human operators to manually tag each image while maintaining or improving tagging accuracy through systematic color analysis.
Solution Approach 2:
The system enables images to self-tag their own color information through automated analysis. The image processing algorithm extracts color data directly from the image content without external human intervention, allowing the system to service itself in terms of metadata generation. This self-service approach dramatically improves scalability while preserving measurement precision.
2Productivity
If automated image processing is implemented to improve scalability, then tagging productivity increases, but the complexity of the system increases
Solution Approach 1:
The patent transforms the color representation parameters from the standard RGB color space to the HSV (Hue, Saturation, Value) color space. This parameter change enables more effective color classification and histogram-based analysis. By changing the parameter system, the patent simplifies the automated color determination process while maintaining high productivity, as the HSV space naturally separates color attributes that are important for product classification.
3Ease of operation
If color-based filtering is implemented, then user experience is improved by reducing search complexity, but accurate color matching becomes more difficult due to shades and variations
Solution Approach 1:
The patent performs preliminary color analysis by generating color histograms and determining dominant colors before the actual product search and filtering operations. This preliminary action creates a robust color reference that accounts for variations and shades in advance, enabling more accurate color matching during user searches. The system prepares color data in advance to facilitate easier and more accurate color-based filtering.
Data Source
AI summary
Various embodiments provide a method for determining color information for an image. For example, a color descriptor for an image can be determined and compared against color descriptors stored for each of a number of sample images, which each represent a color in a color space. Upon comparison, matching scores can be generated for a color match between the image and each respective sample image. In this example, the number of sample images with a matching score above a threshold value can be summed and the image can be assigned to a color associated with a highest frequency of the number of sample images. Accordingly, the assigned color of the image can then be used in a “query by color” search or a browse-by-color capability.