Image Search Clustering by Binary Word Similarity
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Solution Overview
Problem
Traditional search techniques are limited in handling different types of content, particularly images, as they often intermix results, making it difficult for users to find specific images of interest.
Innovation Solution
The implementation of image search techniques that order images based on similarity by clustering them using binary words to describe content, allowing for ranking of search results to prioritize more common and relevant images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional search techniques are used to provide search results, then the search engine can handle a wide variety of content types, but the results intermix different types of images making it difficult for users to find specific images of interest
Solution Approach 1:
The patent segments image search results by clustering images with similar visual content together. Images are grouped into clusters based on visual similarity metrics, and results are presented as organized clusters rather than a flat mixed list. This segmentation allows users to easily navigate and find specific types of images by visual characteristics.
2Productivity
If images are ordered based on similarity clustering, then users can find specific images more easily, but the search system complexity increases due to binary word generation and comparison operations
Solution Approach 1:
The patent performs preliminary actions by pre-processing images into binary word representations and pre-computing similarity metrics before the actual search query is executed. Image databases are pre-clustered and organized based on visual similarity, so when a search is performed, the system can quickly retrieve and rank pre-organized clusters rather than computing similarities from scratch for each query.
3Measurement precision
If binary words are generated and compared to determine image similarity, then accurate image clustering is achieved, but the processing time and computational resources increase
Solution Approach 1:
The patent uses simplified binary word representations instead of complex continuous visual features. By converting images to discrete binary words that capture essential visual characteristics, the system achieves adequate similarity measurement with significantly reduced computational complexity. These binary representations are computationally inexpensive to generate and compare, enabling fast processing while maintaining sufficient accuracy for clustering purposes.
Data Source
AI summary
Image search techniques are described. In one or more implementations, images in a search result are ordered based at least in part on similarity of the images, one to another. The search result having the ordered images is provided in response to a search request.