Search results for "SIMILARITY"
showing 10 items of 474 documents
Kernel image similarity criterion
2011
This paper presents a family of metrics for assessing image similarity. The methods use the Hilbert-Schmidt Independence Criterion (HSIC) to estimate nonlinear statistical dependence between multidimensional images. The proposed methods have very good theoretical and practical properties. We illustrate the performance in evaluating the quality of natural photographic images, hyperspectral images under different noise levels, in synthetic multiresolution problems, and real pansharpening products.
GEM
2014
The widespread use of digital sensor systems causes a tremendous demand for high-quality time series analysis tools. In this domain the majority of data mining algorithms relies on established distance measures like Dynamic Time Warping (DTW) or Euclidean distance (ED). However, the notion of similarity induced by ED and DTW may lead to unsatisfactory clusterings. In order to address this shortcoming we introduce the Gliding Elastic Match (GEM) algorithm. It determines an optimal local similarity measure of a query time series Q and a subject time series S. The measure is invariant under both local deformation on the measurement-axis and scaling in the time domain. GEM is compared to ED and…
A General Framework for Complex Network-Based Image Segmentation
2019
International audience; With the recent advances in complex networks theory, graph-based techniques for image segmentation has attracted great attention recently. In order to segment the image into meaningful connected components, this paper proposes an image segmentation general framework using complex networks based community detection algorithms. If we consider regions as communities, using community detection algorithms directly can lead to an over-segmented image. To address this problem, we start by splitting the image into small regions using an initial segmentation. The obtained regions are used for building the complex network. To produce meaningful connected components and detect …
Qualitative Comparison of Community Detection Algorithms
2011
Community detection is a very active field in complex networks analysis, consisting in identifying groups of nodes more densely interconnected relatively to the rest of the network. The existing algorithms are usually tested and compared on real-world and artificial networks, their performance being assessed through some partition similarity measure. However, artificial networks realism can be questioned, and the appropriateness of those measures is not obvious. In this study, we take advantage of recent advances concerning the characterization of community structures to tackle these questions. We first generate networks thanks to the most realistic model available to date. Their analysis r…
Semantic HMC for Big Data Analysis
2014
International audience; Analyzing Big Data can help corporations to im-prove their efficiency. In this work we present a new vision to derive Value from Big Data using a Semantic Hierarchical Multi-label Classification called Semantic HMC based in a non-supervised Ontology learning process. We also proposea Semantic HMC process, using scalable Machine-Learning techniques and Rule-based reasoning.
Quantum GestART: Identifying and Applying Correlations between Mathematics, Art, and Perceptual Organization
2020
Mathematics can help analyze the arts and inspire new artwork. Mathematics can also help make transformations from one artistic medium to another, considering exceptions and choices, as well as artists' individual and unique contributions. We propose a method based on diagrammatic thinking and quantum formalism. We exploit decompositions of complex forms into a set of simple shapes, discretization of complex images, and Dirac notation, imagining a world of "prototypes" that can be connected to obtain a fine or coarse-graining approximation of a given visual image. Visual prototypes are exchanged with auditory ones, and the information (position, size) characterizing visual prototypes is con…
Combining feature extraction and expansion to improve classification based similarity learning
2017
Abstract Metric learning has been shown to outperform standard classification based similarity learning in a number of different contexts. In this paper, we show that the performance of classification similarity learning strongly depends on the data format used to learn the model. We then present an Enriched Classification Similarity Learning method that follows a hybrid approach that combines both feature extraction and feature expansion. In particular, we propose a data transformation and the use of a set of standard distances to supplement the information provided by the feature vectors of the training samples. The method is compared to state-of-the-art feature extraction and metric lear…
A methodology for assessing the effect of correlations among muscle synergy activations on task-discriminating information
2013
Delis, Ioannis | Berret, Bastien | Pozzo, Thierry | Panzeri, Stefano; International audience; ''Muscle synergies have been hypothesized to be the building blocks used by the central nervous system to generate movement. According to this hypothesis, the accomplishment of various motor tasks relies on the ability of the motor system to recruit a small set of synergies on a single-trial basis and combine them in a task-dependent manner. It is conceivable that this requires a fine tuning of the trial-to-trial relationships between the synergy activations. Here we develop an analytical methodology to address the nature and functional role of trial-to-trial correlations between synergy activation…
Three-dimensional rigid motion estimation using genetic algorithms from an image sequence in an active stereo vision system
2004
This paper proposes a method for estimating the three-dimensional (3D) rigid motion parameters from an image sequence of a moving object. The 3D surface measurement is achieved using an active stereovision system composed of a camera and a light projector, which illuminates the objects to be analyzed by a pyramid-shaped laser beam. By associating the laser rays with the spots in the two-dimensional image, the 3D points corresponding to these spots are reconstructed. Each image of the sequence provides a set of 3D points, which is modeled by a B-spline surface. Therefore, estimating the 3D motion between two images of the sequence boils down to matching two B-spline surfaces. We consider the…
TESTING SIMILARITY COEFFICIENTS FOR ANALYSIS OF THE FOSSIL RECORD USING CLUSTERING METHODS: THE PALAEOZOIC FLORA AS A STUDY CASE
2020
This paper reports a global methodological approach based on the similarity and clustering methods of the Palaeozoic plant fossil record using a comparative approach between two similarity measures: the Jacard and Raup-Crick Coefficients. The results show that although the Raup-Crick Coefficients clearly have the potential for providing more robust results, the consequences of the extinction processes are better reflected in the similarity analysis based on the Jaccard Coefficients. On the other hand, the cluster analysis based on UPGMA algorithm shows four robust clusters and reveals new evidence for the singularity of Mississippian flora. Finally, the results obtained reveal that similari…