Color-Based Image Search Using Spectrum Histograms and Shape Descriptors
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Solution Overview
Problem
Image histograms fail to distinguish between objects based on shape and texture, are sensitive to noisy interference, and cannot differentiate between generic and specific objects, leading to misinterpretation in content-based image retrieval systems.
Innovation Solution
A computer-implemented system that generates a target color scoring template from user input, creates image color spectrum histograms, and conducts a content-based search to dynamically display relevant images, incorporating color, shape, and text metadata to improve image search results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If color histograms are used for image retrieval, then color-based search capability is improved, but the system cannot distinguish objects based on shape and texture
Solution Approach 1:
The patent combines color histogram extraction with shape descriptor computation and texture feature analysis into a unified image retrieval system. The system merges multiple feature types (color, shape, texture) and integrates them through a combined similarity metric, allowing the system to distinguish objects based on multiple attributes simultaneously while maintaining color-based search capability.
Solution Approach 2:
The patent extends the traditional one-dimensional color histogram approach by adding multiple dimensions: shape descriptors (contour, geometry), texture features (frequency, pattern), and spatial information. This multi-dimensional feature space enables the system to differentiate between objects that have similar colors but differ in shape, texture, or spatial arrangement.
2Measurement precision
If color histograms are used for image retrieval, then color matching is improved, but the system is sensitive to noisy interference such as lighting changes
Solution Approach 1:
The patent transforms color data from RGB space to HSV (Hue, Saturation, Value) space, separating color information (Hue) from brightness information (Value). This parameter transformation makes color matching invariant to lighting changes, as the Hue component remains stable even when overall brightness varies. The system also applies normalization techniques to reduce sensitivity to quantization errors.
Solution Approach 2:
The patent introduces an intermediary color space transformation layer between the raw image data and the retrieval index. By converting colors to a normalized color space that separates chromaticity from luminance, the system creates a buffer that filters out the harmful effects of lighting variations and quantization errors before color matching occurs.
3Adaptability or versatility
If image histograms are used, then color representation is improved, but the system cannot distinguish between generic and specific objects
Solution Approach 1:
The patent segments the image analysis process into distinct modules: color histogram extraction, shape descriptor computation, and texture feature analysis. Each module processes specific visual attributes independently, allowing the system to identify both generic color properties and specific object characteristics. The segmented approach enables fine-grained differentiation between object types while maintaining overall color representation capability.
Solution Approach 2:
The patent applies different analysis methods to different regions and aspects of the image: global color histograms for overall color representation, local shape descriptors for object geometry, and regional texture features for surface characteristics. This local quality analysis enables the system to distinguish specific objects (e.g., a red apple vs. a red box) while maintaining the ability to represent color information generally.
4Ease of operation
If traditional image search interfaces are used, then basic search functionality is provided, but the system lacks quantitative measure of aesthetic appropriateness
Solution Approach 1:
The patent replaces manual visual inspection and subjective aesthetic judgment with automated computational metrics. The system substitutes mechanical search operations with intelligent algorithms that quantitatively evaluate aesthetic properties such as color harmony, contrast, balance, and composition. This substitution provides objective, measurable criteria for aesthetic appropriateness while maintaining ease of use through automated processing.
Data Source
AI summary
A method for dynamically displaying results of simultaneous two dimensional image search comprises: maintaining a first database comprising a set of records, each record comprising an image and an associated set of annotations describing the image, receiving an annotation criterion and a color scheme from a user, generating a target color scoring template from the color scheme, conducting a content based search by searching the first database to identify a set of pertinent images satisfying the annotation criterion, and performing the following operations for each image in the set of pertinent images: resizing a given image to a predefined size before generating an image color spectrum histogram for the given image, adding the given image to a pool of acceptable images if the generated image color spectrum histogram matches with the target color scoring template; and dynamically displaying images in the pool of acceptable images to the user.