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Portrait image segmentation based on improved grabcut algorithm 6th

Portrait image segmentation based on improved grabcut algori...

6th International Conference on Swarm Intelligence, ICSI 2015 held in conjunction with the 2nd BRICS Congress on Computational Intelligence, CCI 2015

作者： Li, Shuai Zheng, Xiaohui Chen, Xianjun Zhan, Yongsong Key Laboratory of Cloud Computing and Complex System Guilin University of Electronic Technology Guilin Guangxi541004 China Guangxi Experiment Center of Information Science Guilin University of Electronic Technology Guilin Guangxi541004 China Key Laboratory of Intelligent Processing of Computer Image and Graphics Guilin University of Electronic Technology Guilin Guangxi541004 China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin Guangxi541004 China Information Engineering School Haikou College of Economics Haikou Hainan571127

ISBN: (纸本)9783319204680

Traditional computer portrait caricature system mainly take the method that exaggerate and deform real images directly, that lead the facial image background also been deformed when exaggerate facial image. If in pretreatment stage, we segmented the characters and background of the input image, and then do the subsequent processing, the problem may be solved. But for better portrait caricature effects, we need an excellent segmentation algorithm. So, we propose an improved Grabcut image segmentation algorithm and use it to extract the prospect character image for exaggeration and deformation. In practical application, we separate deform and exaggerate the foreground characters image with TPS method, then fuse it with the original or new background picture, get the final image. Application proves, the method solves the background deformation problem well, and improves the quality and rate of image segmentation, caricature synthesis effect reality and natural. © Springer International Publishing Switzerland 2015.

关键词： Image segmentation

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Correction to: Meeting report from the fourth meeting of the Computational Modeling in Biology Network (COMBINE)

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Standards in genomic sciences 2018年第1期13卷 17页

作者： Dagmar Waltemath Frank T Bergmann Claudine Chaouiya Tobias Czauderna Padraig Gleeson Carole Goble Martin Golebiewski Michael Hucka Nick Juty Olga Krebs Nicolas Le Novère Huaiyu Mi Ion I Moraru Chris J Myers David Nickerson Brett G Olivier Nicolas Rodriguez Falk Schreiber Lucian Smith Fengkai Zhang Eric Bonnet 1Department of Systems Biology and Bioinformatics University of Rostock Rostock Germany. 2Department of Computing and Mathematical Sciences California Institute of Technology Pasadena CA USA. 4Instituto Gulbenkian de Ciência - IGC Rua da Quinta Grande Oeiras Portugal. 3IPK Gatersleben Gatersleben Germany. 5Department of Neuroscience Physiology and Pharmacology University College London London UK. 6School of Computer Science The University of Manchester Manchester UK. 7Heidelberg Institute for Theoretical Studies Heidelberg Germany. 8EMBL-European Bioinformatics Institute Wellcome Trust Genome Campus Hinxton Cambridge UK. 14The Babraham Institute Babraham Research Campus Cambridge UK. 9Department of preventive medicine Keck School of Medicine University of Southern California Los Angeles CA USA. 10Center for Cell Analysis and Modeling University of Connecticut Health Center Farmington CT USA. 11Department of Electrical and Computer Engineering University of Utah Salt Lake City USA. 12Auckland Bioengineering Institute University of Auckland Auckland New Zealand. 13Systems Bioinformatics VU University Amsterdam Amsterdam The Netherlands. 15Martin Luther University Halle-Wittenberg Halle Germany. 16Monash University Melbourne Australia. Computational Biology Unit Laboratory of Systems Biology National Institute of Allergy and Infectious Diseases NIH Bethesda MD USA. 18Institut Curie Paris France. INSERM U900 75248 Paris France. 20Mines ParisTech 77300 Fontainebleau Paris France.

[This corrects the article DOI: 10.4056/sigs.5279417.].

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Reports of the AAAI 2014 conference workshops

Reports of the AAAI 2014 conference workshops

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作者： Albrecht, Stefano V. Barreto, André M. S. Braziunas, Darius Buckeridge, David L. Cuayáhuitl, Heriberto Dethlefs, Nina Endres, Markus Farahmand, Amir-Massoud Fox, Mark Frommberger, Lutz Ganzfried, Sam Guillet, Sébastien Gil, Yolanda Hunter, Lawrence E. Jhala, Arnav Kersting, Kristian Konidaris, George Lecue, Freddy McIlraith, Sheila Natarajan, Sriraam Noorian, Zeinab Poole, David Ronfard, Rémi Saffiotti, Alessandro Shaban-Nejad, Arash Srivastava, Biplav Tesauro, Gerald Uceda-Sosa, Rosario Van Den Broeck, Guy Van Otterlo, Martijn Wallace, Byron C. Weng, Paul Wiens, Jenna Zhang, Jie School of Informatics University of Edinburgh United Kingdom Department of Applied and Computational Mathematics Brazilian National Laboratory Brazil Big Data Group Kobo Inc. Brazil Department of Epidemiology and Biostatistics McGill University Canada School of Mathematical and Computer Sciences Heriot-Watt University in Edinburgh United Kingdom University of Augsburg Germany Carnegie Mellon University United States University of Toronto Canada Cognitive Systems Group University of Bremen Germany LIARA UQAC Chicoutimi Canada Information Sciences Institute University of Southern California Computer Science Department United States Center for Computational Pharmacology University of Colorado School of Medicine United States Department of Computational Media University of California Santa Cruz United States Computer Science Department Technical University of Dortmund Germany MIT Computer Science and Artificial Intelligence Laboratory CambridgeMA United States IBM Research - Smarter Cities Technology Centre Dubline Ireland School of Informatics and Computing Indiana University United States Department of Computer Science University of Saskatchewan Canada University of British Columbia Canada INRIA University of Grenoble France Orebro University Sweden McGill Clinical and Health Informatics Group McGill University Canada IBM Research United States Cognitive Computing Department IBM Research Yorktown HeightsNY United States Computer Science Department Katholieke Universiteit Leuven Belgium Cognitive Artificial Intelligence Department Radboud University Nijmegen Netherlands University of Texas Austin United States Pierre and Marie Curie University Paris France University of Michigan United States School of Computer Engineering Nanyang Technological University Singapore

The AAAI-14 Workshop program was held Sunday and Monday, July 27-28, 2014, at the Québec City Convention Centre in Québec, Canada. The AAAI-14 workshop program included 15 workshops covering a wide range of topics in artificial intelligence. The titles of the workshops were Artificial Intelligence and Robotics;Artificial Intelligence Applied to Assistive Technologies and Smart Environments;Cognitive computing for Augmented Human Intelligence;computer Poker and Imperfect Information;Discovery Informatics;Incentives and Trust in Electronic Communities;Intelligent Cinematography and Editing;Machine Learning for Interactive Systems: Bridging the Gap Between Perception, Action, and Communication;Modern Artificial Intelligence for Health Analytics;Multiagent Interaction Without Prior Coordination;Multidisciplinary Workshop on Advances in Preference Handling;Semantic Cities - Beyond Open Data to Models, Standards, and Reasoning;Sequential Decision Making with Big Data;Statistical Relational AI;and the World Wide Web and Public Health Intelligence. This article presents short summaries of those events. © 2015, Association for the Advancement of Artificial Intelligence. All rights reserved.

关键词： Decision making

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An improved method for semantic similarity calculation based on stop-words 13

An improved method for semantic similarity calculation based...

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13th International Conference on Machine Learning and Cybernetics, ICMLC 2014

作者： Li, Haodi Chen, Qingcai Wang, Xiaolong Intelligent Computing Research Center Computer Science and Technology Harbin Institute of Technology Shenzhen Graduate School Shenzhen China

ISBN: (纸本)9783662456514

Text similarity calculation has become one of the key issues of many applications such as information retrieval, semantic disambiguation, automatic question answering. There are increasing needs of similarity calculations in different levels, e.g. characters, vocabularies, syntactic structures and semantic etc. Most of existing semantic similarity algorithms can be categorized into statistical based methods, rule based methods and combination of these two methods. Statistical methods use knowledge bases to incorporate more comprehensive knowledge and have the capability of reducing knowledge noise. So they are able to obtain better performance. Nevertheless, for the unbalanced distribution of different items in the knowledge base, semantic similarity calculation performance for low-frequency words is usually poor. In this work, based on the distributions of stop-words, we proposes a weights normalization method for semantic dimensions. The proposed method uses the semantic independence of stop-words to avoid semantic bias of corpus in statistical methods. It further improves the accuracy of semantic similarity computation. Experiments compared with several existing algorithms show the effectiveness of the proposed method. © Springer-Verlag Berlin Heidelberg 2014.

关键词： Semantics

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Separating decision tree complexity from subcube partition complexity

Separating decision tree complexity from subcube partition c...

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18th International Workshop on ApproximationAlgorithms for Combinatorial Optimization Problems, APPROX 2015, and 19th International Workshop on Randomization and Computation, RANDOM 2015

作者： Kothari, Robin Racicot-Desloges, David Santha, Miklos Center for Theoretical Physics Massachusetts Institute of Technology CambridgeMA United States David R. Cheriton School of Computer Science Institute for Quantum Computing University of Waterloo WaterlooON Canada Département de Mathématiques Faculté des Sciences Université de Sherbrooke SherbrookeQC Canada Centre for Quantum Technologies National University of Singapore Singapore Singapore LIAFA CNRS Université Paris Diderot Paris France

ISBN: (纸本)9783939897897

The subcube partition model of computation is at least as powerful as decision trees but no separation between these models was known. We show that there exists a function whose deterministic subcube partition complexity is asymptotically smaller than its randomized decision tree complexity, resolving an open problem of Friedgut, Kahn, and Wigderson ([7]). Our lower bound is based on the information-theoretic techniques first introduced to lower bound the randomized decision tree complexity of the recursive majority function. We also show that the public-coin partition bound, the best known lower bound method for randomized decision tree complexity subsuming other general techniques such as block sensitivity, approximate degree, randomized certificate complexity, and the classical adversary bound, also lower bounds randomized subcube partition complexity. This shows that all these lower bound techniques cannot prove optimal lower bounds for randomized decision tree complexity, which answers an open question of Jain and Klauck ([9]) and Jain, Lee, and Vishnoi ([10]). © Robin Kothari, David Racicot-Desloges, and Miklos Santha.

关键词： Decision trees

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Web service request distribution based on object set

Web service request distribution based on object set

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2013 IEEE Conference Anthology, ANTHOLOGY 2013

作者： Zhao, Tianhai High Performance Computing Center School of Computer Science and Technology Northwestern Polytechnical University Xi'an 710072 China

ISBN: (纸本)9781479916603

Aiming at the workload features of web service business request, the request distribution algorithm based on object set is put forward in web service cluster system. The content distribution of web service request, the workload of web service server and the performance degradation of web service server are taken into account in this algorithm. Based on the system model of queuing theory, the system model is put forward and the cost function and reward function are defined and the optimized solution is obtained. With the least cost of request migration of the system, the algorithm can select the server with best performance. Experimental results show that the average response time of the proposed algorithm is less than LARD by 12.2% and throughout is improved by 18%. © 2013 IEEE.

关键词： Web services

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Recognition of facial expressions based on tracking and selection of discriminative geometric features

International Journal of Multimedia and Ubiquitous Engineeri...

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International Journal of Multimedia and Ubiquitous Engineering 2015年第3期10卷 35-44页

作者： Ghimire, Deepak Lee, Joonwhoan Li, Ze-Nian Jeong, Sunghwan Park, Sang Hyun Choi, Hyo Sub Center of KETI Korea Electronics Technology Institute Jeonju-siJeollabuk-do Korea Republic of Dept. of Computer Engineering Chonbuk National University Jeonju-siJeollabuk-do Korea Republic of School of Computing Science Simon Fraser University BurnabyBC Canada

In this paper, we present a method for fully automatic facial expression recognition in facial image sequences using feature extracted from tracking of facial landmarks. The facial landmarks at the first frame of the image sequence under examination are initialized using elastic bunch graph matching (EBGM) algorithm and tracked in the consecutive video frame over time. At first, the most discriminative geometric features in terms of triangle are selected using multi-class AdaBoost with extreme learning machine (ELM) classifier. The features for facial expression recognition (FER) are extracted from AdaBoost selected most discriminative set of triangles composed of facial landmarks. Finally, the facial expressions are recognized using support vector machines (SVM) classification. The results on the extended Cohn-Kanade (CK+) and Multimedia Understanding Group (MUG) facial expression database shows a recognition accuracy of 97.80% and 95.50% respectively using proposed facial expression recognition system. © 2015 SERSC.

关键词： Adaptive boosting

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DDSF: A data deduplication system framework for cloud environments

DDSF: A data deduplication system framework for cloud enviro...

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4th International Conference on Cloud computing and Services science, CLOSER 2014

作者： Gu, Jianhua Zhang, Chuang Zhang, Wenwei High Performance Computing R and D Center School of Computer Science and Technology Northwestern Polytechnical University Xi'an China

ISBN: (纸本)9789897580192

Cloud storage has been widely used because it can provide seemingly unlimited storage space and flexible access way, while the rising cost of storage and communications is an issue. In this paper, we propose a Data Deduplication System Framework(DDSF) for cloud storage environments. The DDSF consists of three major components, the client, fingerprint server and storage component. The client component divides file into chunks and calculates hash value of each chunk. And it only sends the chunk whose hash value has not existed in fingerprint server component to the storage component, so it can reduce consumption of communications and storage space. We developed an Index File System(IFS) to manage the metadata of user file in fingerprint server. The fingerprint server component maintains a hash table containing the hash values of chunks already stored in storage component. This paper presents a two-level indexing mechanism to improve the both spatial and temporal overhead to access to the hash table. The first level employs the Bloom Filter and the second level uses hash table partition mechanism. In order to reduce the possibility of accidental collision of data block hash value, we use two hash algorithms to calculate the hash value of the chunk. We further conduct some experiments in our framework. The results of these experiments demonstrate that framework proposed in this paper can improve storage space utilization and reduce consumption of communications. Copyright © 2014 SCITEPRESS - science and technology Publications.

关键词： Cloud storage

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A multi-channel SSVEP-based brain-computer interface using a canonical correlation analysis in the frequency domain

Advances in Intelligent Systems and Computing

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Advances in Intelligent Systems and computing 2014年 215卷 603-616页

作者： Chen, Guang Song, Dandan Liao, Lejian Beijing Eng. Research Center of High Volume Language Inf. Processing and Cloud Computing App. Beijing Lab of Intelligent Information Technology School of Computer Science Beijing Institute of Technology Beijing China

Brain-computer interface (BCI) is a new way for man-machine interaction with wide applications, in which steady-state visual evoked potentials (SSVEP) is a promising option. However, many characteristics of SSVEP show great user variation. So parameter optimization and channel selection for each subject were applied to improve the performance of BCI. These optimizations limit the practical applicability of the SSVEP-based BCI. The use of a canonical correlation analysis (CCA) method for multi-channel SSVEP in the time domain detection showed highly increased detection accuracy, but it is sensitive to the noise when the stimulate frequency is low. In this paper, a method of CCA in the frequency domain is presented for classifying multi-channel SSVEPs. First overlapping average is conducted on the original training signals. Then fast Fourier transform (FFT) is used to transform the signals from time domain to frequency domain to produce the reference data. Finally, according to the correlation coefficients of the new data and the references in the frequency domain, the SSVEP is classified. The experimental results show the enhanced accuracy of our method when applied to low stimulate frequencies. © Springer-Verlag Berlin Heidelberg 2014.

关键词： Brain computer interface

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Generating software test data by particle swarm optimization

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in computer science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2014年 8886卷 37-47页

作者： Jia, Ya-Hui Chen, Wei-Neng Zhang, Jun Li, Jing-Jing School of Information Science and Technology Sun Yat-sen University Guangzhou China School of Advanced Computing Sun Yat-sen University Guangzhou China Key Lab. Machine Intelligence and Advanced Computing Ministry of Education China Engineering Research Center of Supercomputing Engineering Software MOE China School of Computer Science South China Normal University Guangzhou China

Search-based method using meta-heuristic algorithms is a hot topic in automatic test data generation. In this paper, we develop an automatic test data generating tool named particle swarm optimization data generation tool (PSODGT). The PSODGT is characterized by the following two features. First, the PSODGT adopts the condition-decision coverage (C/DC) as the criterion of software testing, aiming to build an efficient test data set that covers all conditions. Second, the PSODGT uses a particle swarm optimization (PSO) approach to generate test data set. In addition, a new position initialization technique is developed for PSO. Instead of initializing the test data randomly, the proposed technique uses the previously-found test data that can reach the target condition as the initial positions so that the search speed of PSODGT can be further accelerated. The PSODGT is tested on four practical programs. Experimental results show that the proposed PSO approach is promising. © Springer International Publishing Switzerland 2014.

关键词： Software testing

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