Real-Time Reverse Image Search Using Vector Dimensionality Reduction
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Solution Overview
Problem
Existing reverse image search technologies are limited in their ability to efficiently search for visually similar images and videos, as they rely on keywords and do not effectively utilize advanced image recognition techniques to provide real-time results from digital libraries.
Innovation Solution
A real-time reverse image search system that uses a processor to calculate mathematical representations of image patterns, reduces vector dimensions by more than 90% using convolutional neural networks, and clusters vectors to create a searchable subspace, allowing for enhanced image and video searches within digital libraries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If reverse image search uses traditional keyword-based methods, then it can search for images, but it cannot effectively find visually similar images and videos in real-time from digital libraries
Solution Approach 1:
The patent replaces traditional keyword-based mechanical search methods with convolutional neural networks that automatically extract visual features from images and videos. This substitution enables the system to understand visual content semantically rather than relying on manual keyword tagging, achieving both high accuracy in finding visually similar content and real-time search performance through efficient feature extraction and vector comparison
Solution Approach 2:
The patent transforms images and videos into mathematical vectors by changing the parameter representation from pixel data to feature vectors. By converting visual content into compact vector representations that capture essential visual characteristics, the system enables rapid comparison and similarity search across large digital libraries while maintaining high search accuracy
2Productivity
If the system reduces vector dimensions by more than 90%, then the search efficiency improves, but the quality of image representation may deteriorate
Solution Approach 1:
The patent extracts only the most essential visual features from images and videos to create compact vector representations. By selectively extracting dominant visual characteristics rather than preserving all original data, the system achieves over 90% dimension reduction while retaining sufficient information for accurate visual similarity search, balancing efficiency and quality
Solution Approach 2:
The patent transforms high-dimensional image data into low-dimensional vectors by changing the parameter representation space. Through convolutional neural networks, the system identifies and preserves critical visual parameters while discarding redundant information, enabling efficient search with reduced vector dimensions that maintain adequate image quality for similarity matching
3Measurement precision
If the system processes full-resolution images for reverse search, then the search accuracy is high, but the processing time and computational resources increase significantly
Solution Approach 1:
The patent segments images into smaller regions and extracts features from these segments using convolutional neural networks. By processing segmented regions rather than entire high-resolution images, the system reduces computational complexity and processing time while maintaining search accuracy through aggregation of segment-level features into comprehensive image representations
Solution Approach 2:
The patent extracts essential visual features from images at reduced resolution rather than processing full-resolution pixel data. By taking out only the critical visual information needed for similarity matching, the system achieves fast processing speeds while preserving sufficient detail for accurate reverse image search results
Data Source
AI summary
A real-time reverse image searching for images analogous to a representative frame having patterns, including identifying, on a computing device connected to a network, one of the images as the representative frame as input; providing a processor connected to the network and having access to at least one database comprising vectors associated with respective images and videos, the processor: calculating representations for the patterns in the representative frame to form a representative vector, the representations corresponding to dimensions of the vector; reducing the vector by reducing a number of the dimensions by more than 90% and a maximum trade-off between the quality of the image and the size of vector; detecting, in the at least one database, the vectors most similar to the vector of the representative frame; and offering, in real-time via the network, images and videos associated with the selected vectors. The patterns consist of colors.