An Eigendecomposition Approach to Weighted Graph Matching Problems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Simple fast algorithms for the editing distance between trees and related problems
SIAM Journal on Computing
International Journal of Computer Vision
SUSAN—A New Approach to Low Level Image Processing
International Journal of Computer Vision
Structural Matching by Discrete Relaxation
IEEE Transactions on Pattern Analysis and Machine Intelligence
On a relation between graph edit distance and maximum common subgraph
Pattern Recognition Letters
A New Algorithm for Error-Tolerant Subgraph Isomorphism Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Error Correcting Graph Matching: On the Influence of the Underlying Cost Function
IEEE Transactions on Pattern Analysis and Machine Intelligence
The String-to-String Correction Problem
Journal of the ACM (JACM)
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Earth Mover's Distance as a Metric for Image Retrieval
International Journal of Computer Vision
Machine Learning
Computation of Normalized Edit Distance and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
On Image Classification: City vs. Landscape
CBAIVL '98 Proceedings of the IEEE Workshop on Content - Based Access of Image and Video Libraries
Obstacle avoidance and navigation in the real world by a seeing robot rover
Obstacle avoidance and navigation in the real world by a seeing robot rover
A Metric for Distributions with Applications to Image Databases
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
An Eigenspace Projection Clustering Method for Inexact Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Relational Matching with Local Edit Distance
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
Graph Edit Distance from Spectral Seriation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Watermarking text document images using edge direction histograms
Pattern Recognition Letters
Query by image and video content: a colored-based stochastic model approach
Data & Knowledge Engineering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Negative Samples Analysis in Relevance Feedback
IEEE Transactions on Knowledge and Data Engineering
Graph-Theoretical Methods for Detecting and Describing Gestalt Clusters
IEEE Transactions on Computers
A Corner-Finding Algorithm for Chain-Coded Curves
IEEE Transactions on Computers
Matching point sets with respect to the earth mover’s distance
ESA'05 Proceedings of the 13th annual European conference on Algorithms
Which Components are Important for Interactive Image Searching?
IEEE Transactions on Circuits and Systems for Video Technology
Reduced-reference IQA in contourlet domain
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A coarse-to-fine framework to efficiently thwart plagiarism
Pattern Recognition
Automatic drug image identification system based on multiple image features
ICCCI'10 Proceedings of the Second international conference on Computational collective intelligence: technologies and applications - Volume Part II
Transfer latent variable model based on divergence analysis
Pattern Recognition
Malware classification based on call graph clustering
Journal in Computer Virology
Graph structure analysis based on complex network
Digital Signal Processing
Biview face recognition in the shape-texture domain
Pattern Recognition
Scaling up cosine interesting pattern discovery: A depth-first method
Information Sciences: an International Journal
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This paper presents a novel algorithm for computing graph edit distance (GED) in image categorization. This algorithm is purely structural, i.e., it needs only connectivity structure of the graph and does not draw on node or edge attributes. There are two major contributions: (1) Introducing edge direction histogram (EDH) to characterize shape features of images. It is shown that GED can be employed as distance of EDHs. This algorithm is completely independent on cost function which is difficult to be defined exactly. (2) Computing distance of EDHs with earth mover distance (EMD) which takes neighborhood bins into account so as to compute distance of EDHs correctly. A set of experiments demonstrate that the newly presented algorithm is available for classifying and clustering images and is immune to the planar rotation of images. Compared with GED from spectral seriation, our algorithm can capture the structure change of graphs better and consume 12.79% time used by the former one. The average classification rate is 5% and average clustering rate is 25% higher than the spectral seriation method.