Visual Semantic Complex Network for Image Relevance
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Solution Overview
Problem
Current web image search technologies face challenges in modeling the relevance of images due to their diverse and complex structures, relying on textual information that can lead to irrelevant results and failing to connect images with similar semantic meaning but different visual content, while visual feature-based approaches are limited to near-duplicate images.
Innovation Solution
The Visual Semantic Complex Network (VSCN) system automatically discovers and models image clusters with both semantic and visual consistency, forming a graph structure where images are connected based on their correlations, allowing for more accurate modeling of image relevance and enabling macroscopic-level analysis of web image collections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If textual information is used to index web images, then images can be organized and retrieved, but images with the same keyword may come from irrelevant concepts and exhibit large diversity on visual content
Solution Approach 1:
The patent merges textual information with visual features to create a hybrid indexing system. It combines text-based semantic understanding with visual content analysis, allowing images to be indexed by both keyword relevance and visual similarity, thereby resolving the contradiction between ease of retrieval and relevance accuracy
Solution Approach 2:
The patent introduces visual features as an intermediary between textual keywords and image content. Instead of directly indexing images by text alone, the system uses visual feature extraction as a mediator to bridge the gap between semantic search queries and actual image content, improving retrieval reliability
2Productivity
If visual features and ANN algorithms are used to improve search efficiency, then near-duplicate images can be found quickly, but relevant images with the same semantic meaning but moderate difference in visual content cannot be connected
Solution Approach 1:
The patent adds a semantic dimension to the traditional visual feature space. By introducing text-based semantic features as an additional dimension, the system can retrieve images that are semantically relevant even if their visual features differ moderately, thus expanding semantic coverage while maintaining search efficiency through multi-dimensional indexing
3Reliability
If manual organization of web images is performed, then portions of images can be structured, but the human-defined ontology has inherent discrepancies with dynamic web images and is very expensive to scale
Solution Approach 1:
The patent implements self-service by enabling the system to automatically generate its own ontology and structure from web image data. Instead of relying on manual human organization, the system uses unsupervised learning and clustering algorithms to automatically discover image structures, relationships, and categories, making the process scalable without proportional increases in human labor
Data Source
AI summary
A visual semantic complex network system and a method for generating the system have been disclosed. The system may comprise a collection device configured to retrieve a plurality of images and a plurality of texts associated with the images in accordance with given query keywords; a semantic concept determination device configured to determine semantic concepts of the retrieved images and retrieved texts for the retrieved images, respectively; a descriptor generation device configured to, from the retrieved images and texts, generate text descriptors and visual descriptors for the determined semantic concepts; and a semantic correlation device configured to determine semantic correlations and visual correlations from the generated text and visual descriptor, respectively, and to combine the determined semantic correlations and the determined visual correlations to generate the visual semantic complex network system.