Image Topic Embedding for Search Relevance
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Solution Overview
Problem
Traditional online image search methods struggle to accurately identify and present the most relevant images in response to queries due to limitations in metadata-keyword-tag-based and visual-characteristic-based systems, often returning images that are only tangentially related to the query, especially when keyword tags are varied or the visual information is complex.
Innovation Solution
A framework utilizing embedding learning to associate images with topics by generating image feature vectors and weighted word vectors from visual characteristics and keyword tags, aligning them in a common embedding space to compute relevancy scores, enabling the automatic tagging of images and ranking their relevance to queried keywords.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If keyword tags are manually associated with images, then image search relevance can be improved, but the process becomes time-consuming and labor-intensive
Solution Approach 1:
The system enables images to self-tag by automatically extracting visual characteristics and generating keyword tags without human intervention. The image tagging system processes images independently, extracting features and assigning tags based on visual content analysis, thereby eliminating the need for manual tagging while maintaining high relevance accuracy
Solution Approach 2:
The patent replaces manual mechanical tagging processes with automated computational systems. Visual characteristics are extracted through image processing algorithms, and keyword tags are generated through automated matching against a taxonomy, substituting human labor with computational mechanisms that achieve comparable or superior performance
2Extent of automation
If visual characteristics are extracted from images, then automated image search is enabled, but the system returns only tangentially related images due to wealth of visual information
Solution Approach 1:
The system segments visual characteristics into distinct categories such as object detection, scene analysis, color extraction, and texture analysis. Each segment processes specific visual aspects independently and contributes to the overall image representation, enabling the system to distinguish between dominant and incidental visual elements and return more relevant search results
Solution Approach 2:
The patent applies local quality analysis by examining different regions and aspects of images with varying levels of detail. The system identifies which visual characteristics are most salient and relevant to the query, weighting them appropriately in the search matching process, rather than treating all visual information equally
3Adaptability or versatility
If multiple keyword tags are associated with images, then search coverage is improved, but the system becomes less effective when tags are varied due to multiple items depicted
Solution Approach 1:
The system generates a unified visual characteristic representation that serves multiple search functions simultaneously. This comprehensive representation captures all relevant visual aspects of an image and can be used for various search queries and matching tasks, making the system adaptable to different search scenarios without requiring separate processing for each query type
Data Source
AI summary
A framework is provided for associating images with topics utilizing embedding learning. The framework is trained utilizing images, each having multiple visual characteristics and multiple keyword tags associated therewith. Visual features are computed from the visual characteristics utilizing a convolutional neural network and an image feature vector is generated therefrom. The keyword tags are utilized to generate a weighted word vector (or “soft topic feature vector”) for each image by calculating a weighted average of word vector representations that represent the keyword tags associated with the image. The image feature vector and the soft topic feature vector are aligned in a common embedding space and a relevancy score is computed for each of the keyword tags. Once trained, the framework can automatically tag images and a text-based search engine can rank image relevance with respect to queried keywords based upon predicted relevancy scores.