Cloud-Based Similarity Search Using Hierarchical Feature Vectors
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Solution Overview
Problem
Conventional web-based search engines are inefficient in image similarity searches due to the difference between exact and near matches, leading to scalability issues and poor user experience, as indexing methods struggle to effectively identify images with similar attributes in large corpora.
Innovation Solution
A cloud-based similarity search system using pattern recognition techniques and a hierarchical search tree data structure to organize digital assets by their feature vectors, allowing for efficient identification of similar images through a prioritized breadth-first search and node balancing to maintain efficient data distribution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional document indexing methods are used for image searching, then exact keyword matches can be found efficiently, but near-match similarity searches become inefficient and non-scalable
Solution Approach 1:
The patent segments the image search problem by creating multiple specialized indexes: one for exact keyword matching and another for similarity-based near-matches using feature extraction. This allows the system to handle exact matches and near-matches through separate, optimized pathways rather than a single indexing approach.
Solution Approach 2:
The patent introduces feature vectors as an intermediary representation between the image data and the search query. By converting images into feature vectors and using these as intermediaries for comparison, the system enables efficient similarity searches without requiring direct pixel-by-pixel comparison of entire images.
2Measurement precision
If many image attributes are used to determine similarity, then search result relevance improves, but system efficiency and scalability deteriorate
Solution Approach 1:
The patent extracts only the most relevant features from images to create compact feature vectors, rather than using all possible image attributes. This extraction process selects key characteristics that capture essential similarity information while reducing the dimensionality and complexity of the data that must be processed and stored.
Solution Approach 2:
The patent transforms complex image data into simplified parameter representations (feature vectors) that capture essential characteristics in a condensed form. By changing the parameter representation from raw pixel data to extracted features, the system maintains measurement accuracy while reducing computational complexity.
3Productivity
If few image attributes are used to determine similarity, then search efficiency improves, but result relevance and usefulness deteriorate
Solution Approach 1:
The patent performs preliminary feature extraction and indexing of images before actual search operations. By pre-processing images to extract and store key feature vectors in advance, the system prepares the data in a form that enables fast similarity comparisons during search without requiring complex real-time analysis.
Solution Approach 2:
The patent uses a carefully selected subset of image features that provides sufficient accuracy for practical purposes without the overhead of analyzing all possible attributes. This partial action approach focuses computational resources on the most discriminative features that yield good similarity measurements.
Data Source
AI summary
Techniques for facilitating a similarity search of digital assets (e.g., audio files, image files, video files, etc.) are described. Consistent with some embodiments, a cloud-based search service manages one or more search tree data structures for use in organizing digital assets to make the digital assets searchable. Each digital asset is associated with a feature vector based on the various attributes and/or characteristics of the digital asset. The digital assets are then assigned to leaf nodes in one or more search tree data structures based on a measure of the distance between the feature vector of the digital asset and a virtual feature vector associated with a leaf node. When a search for similar digital assets is invoked, a prioritized breadth first search of a search tree is performed to identify the digital assets having the feature vectors closest in distance to the reference digital asset.