Visual Attribute Extraction for Image Search Precision
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Solution Overview
Problem
Traditional image search techniques, both text-based and visual-based, are limited in generating quality results as they rely heavily on keyword tags and visual similarities, often returning unwanted attributes and missing desired images, especially when specific attributes are not prioritized in the search query.
Innovation Solution
The use of machine learning and deep neural networks to extract and focus on specific visual attributes from images, allowing users to select and refine searches based on attributes like color, composition, texture, and style, enabling more precise image retrieval regardless of image tagging status.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional text-based image search techniques are used, then the search process is simple and fast, but the search results depend heavily on the quality of keyword tags and may miss images with desired visual attributes
Solution Approach 1:
The patent combines text-based search and visual-based search into a hybrid search system. The system extracts visual attributes from query images using machine learning models and combines these with text-based keyword matching, allowing the search to leverage both the speed of text-based search and the accuracy of visual attribute matching.
Solution Approach 2:
The patent segments the image into multiple visual attributes (color, texture, shape, composition) and allows independent search on each attribute. This segmentation enables the system to focus on specific visual characteristics that match user intent while maintaining efficient search performance.
2Measurement precision
If traditional visual-based image search techniques are used, then the search can find images with similar visual characteristics, but the results include unwanted attributes and miss images with specific desired attributes
Solution Approach 1:
The patent implements a dynamic attribute weighting system where users can adjust the importance of different visual attributes (color, texture, shape, etc.) in real-time. The machine learning model dynamically adjusts the search parameters based on user preferences and the specific query image, providing flexible and adaptable search results.
Solution Approach 2:
The patent applies different quality thresholds and matching criteria to different visual attributes based on user preferences. For example, if a user prioritizes color matching, the system applies stricter color matching criteria while being more lenient on other attributes, allowing versatile search across different attribute importance levels.
3Adaptability or versatility
If visual-based search is used without attribute selection, then the search covers all visual attributes, but it returns images with unwanted attributes and ignores images with desired attributes
Solution Approach 1:
The patent extracts and isolates specific visual attributes from the query image that are most relevant to user intent. By using machine learning to identify and extract key visual characteristics, the system can focus the search on these extracted attributes while ignoring less important ones, improving result relevance without sacrificing search scope.
Solution Approach 2:
The patent allows users to select only the specific visual attributes they want to search for, rather than analyzing all possible attributes. This partial action approach reduces computational overhead and improves result precision by focusing only on the attributes that matter to the user's specific search intent.
Data Source
AI summary
In various implementations, specific attributes found in images can be used in a visual-based search. Utilizing machine learning, deep neural networks, and other computer vision techniques, attributes of images, such as color, composition, font, style, and texture can be extracted from a given image. A user can then select a specific attribute from a sample image the user is searching for and the search can be refined to focus on that specific attribute from the sample image. In some embodiments, the search includes specific attributes from more than one image.