Multi-Use Vocabulary for Image Retrieval
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Solution Overview
Problem
Existing image retrieval systems face inefficiencies due to the time-consuming process of generating and comparing large quantities of image features, requiring significant memory storage and lacking effective exploration of vocabulary generation, leading to potential inefficiencies and limitations.
Innovation Solution
A multi-use vocabulary is generated from a source dataset, allowing for efficient retrieval from target datasets of varying sizes and types, using techniques like hierarchical clustering and inverted file approaches to reduce computation and memory requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If feature-level comparison is used for image retrieval, then retrieval accuracy is improved, but time consumption and memory requirements increase significantly
Solution Approach 1:
The patent segments the continuous feature space into discrete vocabulary words through clustering. Instead of comparing all possible feature values, the system divides the feature space into clustered regions (vocabular) where each cluster represents a discrete word. This segmentation reduces the comparison space from continuous to discrete, significantly reducing time consumption while maintaining retrieval accuracy through the inverted file structure that efficiently maps words to image indices.
Solution Approach 2:
The patent introduces vocabulary words as an intermediary layer between raw image features and retrieval results. The system first converts image features into vocabulary words through clustering, then performs retrieval based on word comparisons rather than direct feature comparisons. This intermediary representation reduces the dimensionality and complexity of comparisons while preserving the essential semantic information needed for accurate retrieval.
2Measurement precision
If feature-level comparison is used for image retrieval, then retrieval accuracy is improved, but memory storage requirements increase significantly
Solution Approach 1:
The patent segments the continuous feature space into discrete vocabulary words through clustering. Instead of storing and comparing all possible feature values, the system divides the feature space into clustered regions (vocabular) where each cluster represents a discrete word. This segmentation reduces the storage requirements from storing continuous feature vectors to storing discrete word assignments and their corresponding image indices.
Solution Approach 2:
The patent extracts only the essential clustering information needed for retrieval, storing vocabulary assignments for each image rather than complete feature vectors. The inverted file structure extracts and stores only the relevant word-image mappings, eliminating the need to store and process the full continuous feature space, thereby significantly reducing memory storage requirements.
3Adaptability or versatility
If vocabulary size is increased to improve retrieval coverage, then more features are captured, but computation and memory requirements increase
Solution Approach 1:
The patent applies partial action by creating a vocabulary size that is sufficient for retrieval needs without being excessively large. The system determines an optimal vocabulary size that captures the essential feature variations needed for accurate retrieval while avoiding the diminishing returns of overly large vocabularies. This balanced approach ensures adequate retrieval coverage while maintaining computational efficiency and manageable memory requirements.
Data Source
AI summary
Functionality is described for generating a vocabulary from a source dataset of image items or other non-textual items. The vocabulary serves as a tool for retrieving items from a target dataset in response to queries. The vocabulary has at least one characteristic that allows it to be used to retrieve items from multiple different target datasets. A target dataset can have a different size than the source dataset and/or a different type than the source dataset. The enabling characteristic may correspond to a size of the source dataset above a prescribed minimum number of items and/or a size of the vocabulary above a prescribed minimum number of words.