Visual Graph Mining
Quanshi Zhang, Xuan Song, and Ryosuke Shibasaki

TL;DR
This paper introduces a novel approach for mining maximal-size frequent subgraphs in visual data, enabling the discovery of common objects in cluttered, unlabeled images and videos by addressing data fuzziness and variability.
Contribution
It formulates a new definition of visual subgraph patterns and proposes an efficient approximate method for mining these patterns in complex visual data.
Findings
Method successfully mines common patterns in diverse visual datasets
Demonstrates robustness to data fuzziness and variability
Validates generality across videos and RGB/RGB-D images
Abstract
In this study, we formulate the concept of "mining maximal-size frequent subgraphs" in the challenging domain of visual data (images and videos). In general, visual knowledge can usually be modeled as attributed relational graphs (ARGs) with local attributes representing local parts and pairwise attributes describing the spatial relationship between parts. Thus, from a practical perspective, such mining of maximal-size subgraphs can be regarded as a general platform for discovering and modeling the common objects within cluttered and unlabeled visual data. Then, from a theoretical perspective, visual graph mining should encode and overcome the great fuzziness of messy data collected from complex real-world situations, which conflicts with the conventional theoretical basis of graph mining designed for tabular data. Common subgraphs hidden in these ARGs usually have soft attributes, with…
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Taxonomy
TopicsAdvanced Image and Video Retrieval Techniques · Data Management and Algorithms · Graph Theory and Algorithms
