Digital Media Search Refinement via Image Exemplar Context
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Solution Overview
Problem
Users face difficulties in searching and filtering digital media items due to the lack of associated keywords or textual metadata, making it tedious to describe sought media items accurately.
Innovation Solution
A system that obtains user queries, performs searches within digital media item repositories, and updates queries based on context information from user-selected items, allowing refinement of initial queries without requiring additional search terms, using components like query, search, results, selection, and label components to facilitate searching, labeling, and filtering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users perform searches using text queries in digital media item repositories, then search functionality is provided, but search accuracy deteriorates when keywords or textual metadata are lacking
Solution Approach 1:
The patent introduces image exemplars as intermediary objects between the user's search intent and the digital media item repository. Instead of relying solely on text queries that require precise keywords, users provide image exemplars that serve as visual mediators. The system extracts features from these image exemplars and uses them to query the repository, bridging the gap caused by insufficient textual metadata and improving search accuracy in the presence of information loss.
2Ease of operation
If users manually describe digital media items in words, then search queries can be formed, but the process becomes tedious and time-consuming
Solution Approach 1:
The patent replaces the mechanical process of manual text description with an automated image-based system. Instead of users manually typing or describing media item characteristics in words (a tedious mechanical process), the system accepts image exemplars and automatically extracts relevant features through computational image analysis. This substitution eliminates the time-consuming manual description step while maintaining or improving search effectiveness.
Solution Approach 2:
The system enables self-service searching where the image exemplar itself serves as the query mechanism. The image contains inherent visual information that automatically encodes the search criteria, eliminating the need for users to manually translate their search intent into textual descriptions. The system autonomously processes the image exemplar to generate search queries, making the operation easier and faster.
3Adaptability or versatility
If text-based search queries are used, then searching is straightforward, but the ability to capture visual characteristics of desired media items is limited
Solution Approach 1:
The patent transitions the search mechanism from a one-dimensional text-based approach to a multi-dimensional approach by incorporating image exemplars. Image data contains rich visual information across multiple dimensions (color, texture, shape, spatial relationships) that text queries cannot capture. This dimensional expansion enables the system to adapt to diverse visual characteristics of media items while the underlying feature extraction and matching mechanisms handle the increased complexity automatically.
Data Source
AI summary
In certain embodiments, media item search and machine learning system training may be facilitated. In some embodiments, a first set of media items may be obtained (based on performance of a query) and presented on a user interface. A user selection of a media item of the first set may be obtained, and the query may be updated based on the user-selected media item. A second set of media items may be obtained based on performance of the updated query, and media items of the second set may be assigned to a group based on their similarities with one another. A predicted name for the group may be determined via a machine learning system and presented on the user interface. A user-indicated update to the predicted name for the group may be obtained and provided to the machine learning system to train the machine learning system.