the proceedings contain 127 papers. the special focus in this conference is on Visual recognition, Detection, Contours, Lines and Paths. the topics include: Finding clusters and components by unsupervised learning;a d...
ISBN:
(纸本)9783540225706
the proceedings contain 127 papers. the special focus in this conference is on Visual recognition, Detection, Contours, Lines and Paths. the topics include: Finding clusters and components by unsupervised learning;a demonstration on text data;the use of graph techniques for identifying objects and scenes in indoor building environments for mobile robots;graphical-based learning environments for patternrecognition;spectral analysis of complex laplacian matrices;a significant improvement of softassign with diffusion kernels;eigenspace method by autoassociative networks for object recognition;extraction of skeletal shape features using a visual attention operator;computingthe cyclic edit distance for pattern classification by ranking edit paths;steady state random walks for path estimation;new variational framework for rigid-body alignment;an error-tolerant approximate matching algorithm for attributed planar graphs and its application to fingerprint classification;comparison of algorithms for web document clustering using graph representations of data;a syntactic patternrecognition approach to computer assisted translation;a general methodology for finite-state translation using alignments;a comparison of unsupervised shot classification algorithms for news video segmentation;diagnosis of lung nodule using the semivariogram function;distances between distributions;multiscale curvature assessment of postural deviations;learning people movement model from multiple cameras for behaviour recognition;a comparison of least squares and spectral methods for attributed graph matching and an auction algorithm for graph-based contextual correspondence matching.
this paper presents a new modular neural network architecture that is used to build a system for patternrecognition based on the iris biometric measurement of persons. In this system, the properties of the person iri...
详细信息
ISBN:
(纸本)9783642253294;9783642253300
this paper presents a new modular neural network architecture that is used to build a system for patternrecognition based on the iris biometric measurement of persons. In this system, the properties of the person iris database are enhanced with image processing methods, and the coordinates of the center and radius of the iris are obtained to make a cut of the area of interest by removing the noise around the iris. the inputs to the modular neural network are the processed iris images and the output is the number of the identified person. the integration of the modules was done with a type-2 fuzzy integrator at the level of the sub modules, and with a gating network at the level of the modules.
the paper presents methods for creating and understanding user behavior patterns. Kmeans algorithm is used for users grouping. After that, the sequential associative rules are built for each cluster separately. the co...
详细信息
ISBN:
(纸本)9781728140681
the paper presents methods for creating and understanding user behavior patterns. Kmeans algorithm is used for users grouping. After that, the sequential associative rules are built for each cluster separately. the concept of association rules is introduced;the method of finding dependencies is developed. the accuracy of model is evaluated.
Automatic kinship verification system is designed to verify kinship relations between given pair of face images. Child adopts many characteristic such as similarity in appearance, likes and dislike, behavior, voice fr...
详细信息
ISBN:
(纸本)9781538659069
Automatic kinship verification system is designed to verify kinship relations between given pair of face images. Child adopts many characteristic such as similarity in appearance, likes and dislike, behavior, voice from his/her parents due to overlapping of genes. there are various existing algorithms that can verify whether a given pair of face images share kinship relation. this paper proposes a new method based on compound local binary pattern (CLBP) and local feature-based discriminate analysis (LFDA) to improve kinship verification accuracy. A well known texture feature extraction method is local binary pattern (LBP), but LBP performance deteriorates in flat images. To overcome this drawback, extraction of texture features from face images are calculated by compound local binary pattern (CLBP) technique. Extracted features mainly represent facial characteristics but may also contain some noises. Further, local feature-based discriminate analysis (LFDA) is used as a feature selection method to reduce these noises and choose the most relevant facial features. LFDA reduces inter-class similarity and increases intra-class similarity. Proposed method uses KNN classifier with 5-fold cross-validation. Kinship images are collected from KinFaceW-I and KinFaceW-II dataset. Best mean accuracy for proposed method on KinFaceW-I and KinFaceW-II are 82.825 % and 89.36% respectively. Experimental results also outperforms existing methods on these datasets.
the aim of this work is to propose a new approach to the recognition of historical texts by providing an adaptive mechanism that automatically tunes itself to a specific book. the system is based on clustering togethe...
详细信息
Following the constantly increasing adoption of affective computing based solutions, this paper investigates the feasibility of multilingual anger identification. To this end, we formed such a corpus by suitably combi...
详细信息
ISBN:
(纸本)9781665429337
Following the constantly increasing adoption of affective computing based solutions, this paper investigates the feasibility of multilingual anger identification. To this end, we formed such a corpus by suitably combining seven different datasets representing five different languages, i.e. English, German, Italian, Urdu, and Persian. After analyzing the diverse characteristics of the datasets, we designed four classification algorithms, namely Support Vector Machine, Decision Tree-based Bagging scheme, Convolutional Neural Network, and Convolutional Recurrent Neural Network. Such classification mechanisms are trained on appropriate features extracted from time and/or frequency domains, while speech data have been balanced considering every diverse characteristic incorporated in the datasets (language, sex, acted, etc.). Our findings render multilingual anger identification feasible since the proposed audio patternrecognition methodology based on Mel-spectrograms and CRNN achieved quite satisfactory identification rates.
this study addresses the increasingly encountered challenge of data clustering. We present a comparative study to data clustering for cloud computing using Fuzzy C-MEANS and Adaptive Resonance theory. To reduce varian...
详细信息
this study addresses the increasingly encountered challenge of data clustering. We present a comparative study to data clustering for cloud computing using Fuzzy C-MEANS and Adaptive Resonance theory. To reduce variance and improve generalization ability, we used a resampling method based on 10-fold cross-validation. the typical initialization scheme is applied to improve the convergence speed of training and thus, reach the optimal solution. Experimental results on cloud computing datasets showed that the typical initialization-based Fuzzy Adaptive Resonance theory model is effective and achieves improved accuracy for patternrecognition task compared to Fuzzy C-MEANS. (C) 2021the Authors. Published by Elsevier B.V.
the aim of our study is to examine whether the overall organization of behavior differs when people report truthful vs. deceptive messages within the framework of the T-pattern model. We tested the hypothesis that the...
详细信息
Aimed at the serious problem of the microblog platform used in the field of evaluating TV program that is badly affected by spam microblog, this paper proposes a recognition method about combination of lexicon match w...
详细信息
ISBN:
(纸本)9783662490143;9783662490136
Aimed at the serious problem of the microblog platform used in the field of evaluating TV program that is badly affected by spam microblog, this paper proposes a recognition method about combination of lexicon match with SVM based on pattern matching and machine learning. At the same time, considering the impact that spam information caused in the public-opinion-trend and topic-attention-degree, it is important to identify the spam microblog correctly. they are various cleaning modes for different spam information. And the results of experiment shows that the total-recognition-rate has already reached 80%. this method is useful for the following text mining.
暂无评论