Faceted Image Clustering via Probabilistic Topic Models
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Solution Overview
Problem
Traditional image search and browsing tools lack organization and structure, making it difficult for users to efficiently find specific images among a large volume of digital images, as they return numerous irrelevant results that users must sift through manually.
Innovation Solution
The implementation of a faceted search system that clusters images using visual features and properties, represented by a map-reduce based probabilistic model, allowing users to select and drill down into clusters of interest, with representative images identifying cluster membership probabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional text-based search tools are used to return all images relevant to a query, then the user is provided with comprehensive results, but the user must sift through hundreds or thousands of images to find images of interest, which is frustrating and time-consuming
Solution Approach 1:
The patent segments the large set of search results into multiple clusters based on visual similarity. Each cluster groups together images that share common visual characteristics, allowing users to navigate through organized groups rather than individual images. This segmentation reduces the time to find desired images by providing a structured overview of the result set.
Solution Approach 2:
The patent introduces a new dimension of organization by creating visual clusters that group images based on their visual features. This adds a hierarchical structure to the search results, transforming the flat list of images into a multi-level navigation system where users can first select a cluster and then browse images within that cluster.
2Quantity of substance
If traditional image search tools present a large number of images without structure or organization, then all relevant images are displayed, but the user experience is degraded due to the lack of organization making it difficult to locate desired images
Solution Approach 1:
The patent segments the unorganized image set into structured clusters based on visual similarity. Each cluster represents a coherent group of images sharing common visual characteristics, making it easier for users to locate desired images by navigating through organized groups rather than searching through an unstructured list.
Solution Approach 2:
The patent performs preliminary organization of images into clusters before presenting them to the user. This pre-processing step groups images by visual similarity, so that when users receive search results, they are already organized in a way that facilitates easy location of desired images without requiring manual sifting.
3Ease of operation
If the system creates multiple image clusters based on visual features, then images are well-organized and easier to browse, but the computational complexity of processing and clustering large-scale images increases
Solution Approach 1:
The patent segments the large-scale image processing task into smaller, manageable clusters. By dividing the computation into multiple parallel processing streams that each handle a subset of images, the system reduces the computational complexity of any single processing unit while still achieving comprehensive clustering of all images.
Solution Approach 2:
The patent implements a multi-stage clustering process where images are first grouped into coarse clusters, and then further refined into sub-clusters. This partial action approach allows the system to provide useful organization at each stage without requiring complete processing of all images to the finest level, reducing overall computational complexity while maintaining browsing effectiveness.
Data Source
AI summary
Disclosed herein is a system and method that facilitate searching and/or browsing of images by clustering, or grouping, the images into a set of image clusters using facets, such as without limitation visual properties or visual characteristics, of the images, and representing each image cluster by a representative image selected for the image cluster. A map-reduce based probabilistic topic model may be used to identify one or more images belonging to each image cluster and update model parameters.