The Earth Mover's Distance as a Metric for Image Retrieval
International Journal of Computer Vision
Efficient Color Histogram Indexing for Quadratic Form Distance Functions
IEEE Transactions on Pattern Analysis and Machine Intelligence
A fast, non-iterative and exact histogram matching algorithm
Pattern Recognition Letters
Improving similarity measures of histograms using smoothing projections
Pattern Recognition Letters
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Efficient matching of large-size histograms
Pattern Recognition Letters
Object and image indexing based on region connection calculus and oriented matroid theory
Discrete Applied Mathematics - Special issue: Advances in discrete geometry and topology (DGCI 2003)
Integrating Relevance Feedback Techniques for Image Retrieval Using Reinforcement Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hidden annotation for image retrieval with long-term relevance feedback learning
Pattern Recognition
Image retrieval based on incremental subspace learning
Pattern Recognition
Minimum Cross-Entropy Pattern Classification and Cluster Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image retrieval based on histogram of fractal parameters
IEEE Transactions on Multimedia
A Normalized Levenshtein Distance Metric
IEEE Transactions on Pattern Analysis and Machine Intelligence
A new similarity measure for histogram comparison and its application in time series analysis
Pattern Recognition Letters
Content based radiology image retrieval using a fuzzy rule based scalable composite descriptor
Multimedia Tools and Applications
A new algorithm to compute the distance between multi-dimensional histograms
CIARP'07 Proceedings of the Congress on pattern recognition 12th Iberoamerican conference on Progress in pattern recognition, image analysis and applications
Palmprint identification using pairwise relative angle and EMD
VISUAL'07 Proceedings of the 9th international conference on Advances in visual information systems
Texture classification by modeling joint distributions of local patterns with Gaussian mixtures
IEEE Transactions on Image Processing
A coarse-to-fine framework to efficiently thwart plagiarism
Pattern Recognition
Heat diffusion based dissimilarity analysis for schizophrenia classification
PRIB'11 Proceedings of the 6th IAPR international conference on Pattern recognition in bioinformatics
A fast and exact modulo-distance between histograms
SSPR'06/SPR'06 Proceedings of the 2006 joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
An efficient distance between multi-dimensional histograms for comparing images
SSPR'06/SPR'06 Proceedings of the 2006 joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
A fast distance between histograms
CIARP'05 Proceedings of the 10th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis and Applications
Online text-independent writer identification based on stroke's probability distribution function
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
A hierarchical semantic-based distance for nominal histogram comparison
Data & Knowledge Engineering
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The aim of this paper is to present a new method to compare histograms. The main advantage is that there is an important time-complexity reduction with respect to the methods presented before. This reduction is statistically and analytically demonstrated in the paper. The distances between histograms that we presented are defined on a structure called signature, which is a lossless representation of histograms. Moreover, the type of the elements of the sets that the histograms represent are ordinal, nominal and modulo. We show that the computational cost of these distances is O(z^') for the ordinal and nominal types and O(z^'^2) for the modulo one, z^' being the number of non-empty bins of the histograms. The computational cost of the algorithms presented in the literature depends on the number of bins of the histograms. In most of the applications, the obtained histograms are sparse; then considering only the non-empty bins drastically decreases the time consumption of the comparison. The distances and algorithms presented in this paper are experimentally validated on the comparison of images obtained from public databases and positioning of mobile robots through the recognition of indoor scenes (captured in a learning stage).