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检索条件"任意字段=26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR"

共 1569 条记录，以下是1071-1080 订阅

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Simultaneous Active Learning of Classifiers & Attributes via Relative Feedback

Simultaneous Active Learning of Classifiers & Attributes via...

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Biswas, Arijit Parikh, Devi Univ Maryland College Pk MD 20742 USA Virginia Tech Blacksburg VA USA

ISBN: (纸本)9780769549897

Active learning provides useful tools to reduce annotation costs without compromising classifier performance. However it traditionally views the supervisor simply as a labeling machine. Recently a new interactive learning paradigm was introduced that allows the supervisor to additionally convey useful domain knowledge using attributes. the learner first conveys its belief about an actively chosen image e. g. "I think this is a forest, what do you think?". If the learner is wrong, the supervisor provides an explanation e. g. "No, this is too open to be a forest". With access to a pre-trained set of relative attribute predictors, the learner fetches all unlabeled images more open than the query image, and uses them as negative examples of forests to update its classifier this rich human-machine communication leads to better classification performance. In this work, we propose three improvements over this set-up. First, we incorporate a weighting scheme that instead of making a hard decision reasons about the likelihood of an image being a negative example. Second, we do away with pre-trained attributes and instead learn the attribute models on the fly, alleviating overhead and restrictions of a pre-determined attribute vocabulary. Finally, we propose an active learning framework that accounts for not just the label-but also the attributes-based feedback while selecting the next query image. We demonstrate significant improvement in classification accuracy on faces and shoes. We also collect and make available the largest relative attributes dataset containing 29 attributes of faces from 60 categories.

关键词： image retrieval interactive systems learning (artificial intelligence) man-machine systems pattern classification

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Affectiva-MIT Facial Expression Dataset (AM-FED): Naturalistic and Spontaneous Facial Expressions Collected In-the-Wild

Affectiva-MIT Facial Expression Dataset (AM-FED): Naturalist...

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： McDuff, Daniel El Kaliouby, Rana Senechal, thibaud Amr, May Cohn, Jeffrey F. Picard, Rosalind MIT Media Lab Cambridge MA 02139 USA Affect Inc Waltham MA 02454 USA Carnegie Mellon Univ Inst Robot Pittsburgh PA 15106 USA

ISBN: (纸本)9780769549903

computer classification of facial expressions requires large amounts of data and this data needs to reflect the diversity of conditions seen in real applications. Public datasets help accelerate the progress of research by providing researchers with a benchmark resource. We present a comprehensively labeled dataset of ecologically valid spontaneous facial responses recorded in natural settings over the Internet. To collect the data, online viewers watched one of three intentionally amusing Super Bowl commercials and were simultaneously filmed using their webcam. they answered three self-report questions about their experience. A subset of viewers additionally gave consent for their data to be shared publicly with other researchers. this subset consists of 242 facial videos (168,359 frames) recorded in real world conditions. the dataset is comprehensively labeled for the following: 1) frame-by-frame labels for the presence of 10 symmetrical FACS action units, 4 asymmetric (unilateral) FACS action units, 2 head movements, smile, general expressiveness, feature tracker fails and gender;2) the location of 22 automatically detected landmark points;3) self-report responses of familiarity with, liking of, and desire to watch again for the stimuli videos and 4) baseline performance of detection algorithms on this dataset. this data is available for distribution to researchers online, the EULA can be found at: http://***/facial-expression-dataset-am-fed/.

关键词： face recognition video signal processing

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On Controlling Genuine Reject Rate in Multi- stage Biometric Verification

On Controlling Genuine Reject Rate in Multi- stage Biometric...

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Hossain, Md S. Balagani, Kiran S. Phoha, Vir V. Louisiana Tech Univ Ruston LA 71270 USA New York Inst Technol Old Westbury NY 11568 USA

ISBN: (纸本)9780769549903

An important problem in multi-stage biometric verification is to select an appropriate reject region. A reject region says which samples to be rejected. Rejecting impostor samples does not incur any cost in terms of user inconvenience, however, erroneously rejecting genuine samples leads to both user and administrator inconvenience. the problem becomes severe in the applications that involve a huge number of biometric transactions. Such applications necessitate the reject rate of genuine samples to be controlled. However, to date, no work has studied on controlling genuine reject rate (GRR) in multi-stage biometric verification. In this paper, we focused on controlling GRR and to this end, developed a rejection method called symmetric rejection method. Our rejection method adds the following benefits to multi-stage biometric verification: (1) it enables the system administrator to control GRR, (2) it allows to calculate the reject region without estimation of score distributions, and (3) it does not use any assumption on the functional form of score distributions. We performed experiments on (1) two fingerprint datasets of 6000 users and (2) two face datasets of 3000 users. For fingerprint data, we achieved 18.96 percent to 70.89 percent reduction in EER by rejecting 1.5 percent to 9.4 percent genuine scores and for face data, we achieved 3.27 percent to 85.83 percent reduction in EER by rejecting 0.3 percent to 14.4 percent genuine scores.

关键词： face recognition fingerprint identification

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Annual International conference of the ieee Engineering in Medicine and Biology Society

作者： Ricardo Sousa Daniel C. Moura Mario Dinis-Ribeiro Miguel T. Coimbra Instituto de Engenharia Biomedica Universidade do Porto Instituto de Telecomunicacoes da Universidade do Porto CINTESIS/Faculdade de Medicina da Universidade do Porto

ISBN: (纸本)9781424479276

Local descriptors coupled with robust methods for learning visual dictionaries have been a pivotal tool in computer vision. Although the identification of similar patterns is commonly conducted on some stage of the bag-of-words framework, a prior assessment of spatial local similarities can be indicative of specific objects, and thus improved recognition rates. In this work we delve a function of similarity for enhancing the discriminative power of local constrained SIFT descriptors. Motivated by gastrointestinal images where diagnosis through endoscopy plays a decisive role in cancer detection and resulting prognosis, visual cues in these early stages are slim and of difficult perception. In order to capture these patterns we propose a self-similarity approach (based on a neighbourhood analysis of SIFT descriptors) to assess local variances through a weight function. Based on extensive simulations our approach achieved a performance of 88%: 3% higher than the standard SIFT, 10% higher than Haar wavelet and 13% higher than LBPs.

关键词： computer vision Dictionaries Endoscopes Visualization pattern recognition Gastrointestinal tract Gastroenterology

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Constrained Graph Concept Factorization for Image Clustering

Constrained Graph Concept Factorization for Image Clustering

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第26届中国控制与决策会议

作者： Yuqing Shi Shiqiang Du Weilan Wang School of Electrical Engineering Northwest University for Nationalities School of Mathematics and Computer Science Northwest University for Nationalities

ISBN: (纸本)9781479937097

Matrix factorization techniques have been frequently applied in data representation and pattern recognition. One of them is Concept Factorization(CF), which is a new matrix decomposition technique for data representation. In this paper, we propose a novel semi-supervised matrix factorization algorithm, called Constrained Graph Concept Factorization(CGCF), which incorporates the label information as additional constraints. Specifically, CGCF preserves the intrinsic geometry of data as regularized term and use the label information as semi-supervised learning, it makes nearby samples with the same class-label are more compact, and nearby classes are separated. An efficient multiplicative updating procedure was produced along with its theoretic justification of the algorithmic convergence. Compared with NMF, GNMF, CF, LCCF and Kmeans, experiment results on ORL and YALE face databases have shown that the proposed method achieves better clustering results.

关键词： Data Representation,Concept Factorization(CF),Semi-supervised Learning,Clustering

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AUTOMATIC FACE recognition FROM VIDEO SEQUENCES USING A TEMPLATE BASED CROSS CORRELATION MEthOD

AUTOMATIC FACE RECOGNITION FROM VIDEO SEQUENCES USING A TEMP...

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26th ieee Annual Canadian conference on Electrical and computer Engineering (CCECE)

作者： Rosales, Edward Tie, Yun Venetsanopoulos, Anastasios Guan, Ling Ryerson Univ Toronto ON Canada

ISBN: (纸本)9781479900336;9781479900312

Face recognition in videos has been an active topic in the field of object recognition and computer vision. In this paper we propose an automatic face recognition algorithm from video sequences using a template based cross correlation (TBCC) method. It utilizes random selection of frames to form the training template for the discriminant feature representation of a face. the proposed method was tested by randomly selecting a subject from the RML and Cohn-Kanade (CK) databases with non-overlap sequences between test and training sequences. From the experiments on several datasets, the system performance achieved an average recognition rate of 88%.

关键词： Cross correlation kernel canonical correlation analysis optimal adaptive correlation

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SOFTWARE ARCHITECTURE DECOMPOSITION USING ADAPTIVE K-NEAREST NEIGHBOR ALGORIthM

SOFTWARE ARCHITECTURE DECOMPOSITION USING ADAPTIVE K-NEAREST...

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26th ieee Annual Canadian conference on Electrical and computer Engineering (CCECE)

作者： Alkhalid, Abdulaziz Lung, Chung-Horng Ajila, Samuel Carleton Univ Dept Syst & Comp Engn Ottawa ON K1S 5B6 Canada

ISBN: (纸本)9781479900336;9781479900312

Software architecture decomposition plays an important role in software design cascading effect on various development phases. Software designer decomposes software based on his/her experience. though it may work well for some, in reality many systems failed to meet the requirements as a result of poor design. Software architecture decomposition using clustering techniques has been investigated in software engineering research. this paper presents an enhanced approach for software architecture decomposition. We used two hierarchical agglomerative clustering methods and adaptive K-nearest neighbor algorithm in this enhanced approach and applied it on two industrial software systems. Results show that the approach provides objective and insightful information for software designer.

关键词： Design Software Architecture Clustering pattern recognition Algorithms

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An object representation model based on the mechanism of visual perception

An object representation model based on the mechanism of vis...

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26th International conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2013

作者： Wei, Hui Wang, Ziyan Laboratory of Cognitive Model and Algorithm Department of Computer Science Fudan University Shanghai China

ISBN: (纸本)9783642385766

In areas of artificial intelligence and computer vision, object representation and recognition is an important topic, and lots of methods have been developed for it. However, analysis and obtain the knowledge of object's structure at higher levels are still very difficult now. We draw on the experience of pattern recognition theories of cognitive psychology to construct a compact, abstract and symbolic representing model of object's contour based on components fitting which is more consistent with human's cognition process. In addition, we design an algorithm to match components between different images of the same object in order to make the feature extraction at higher levels possible. © 2013 Springer-Verlag.

关键词： pattern recognition

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AUTOMATIC FACE recognition FROM VIDEO SEQUENCES USING A TEMPLATE BASED CROSS CORRELATION MEthOD

AUTOMATIC FACE RECOGNITION FROM VIDEO SEQUENCES USING A TEMP...

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ieee Canadian conference on Electrical and computer Engineering

作者： Edward Rosales Yun Tie Anastasios Venetsanopoulos Ling Guan Electrical Engineering Ryerson University Toronto ON Canada

ISBN: (纸本)9781479900312

Face recognition in videos has been an active topic in the field of object recognition and computer vision. In this paper we propose an automatic face recognition algorithm from video sequences using a template based cross correlation (TBCC) method. It utilizes random selection of frames to form the training template for the discriminant feature representation of a face. the proposed method was tested by randomly selecting a subject from the RML and Cohn-Kanade (CK) databases with non-overlapping sequences between test and training sequences. From the experiments on several datasets, the system performance achieved an average recognition rate of 88%.

关键词： Facial recognition Video sequences jigs(templates) Videotapes Cross correlation training sequence Randomisation object recognition images computer vision AUTOMATIC System performance face recognition algorithm

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Physically Plausible 3D Scene Tracking: the Single Actor Hypothesis

Physically Plausible 3D Scene Tracking: The Single Actor Hyp...

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ieee conference on computer vision and pattern recognition

作者： Nikolaos Kyriazis Antonis Argyros Institute of Computer Science FORTH and Computer Science Department University of Crete

ISBN: (纸本)9781467364102

In several hand-object(s) interaction scenarios, the change in the objects' state is a direct consequence of the hand's motion. this has a straightforward representation in Newtonian dynamics. We present the first approach that exploits this observation to perform model-based 3D tracking of a table-top scene comprising passive objects and an active hand. Our forward modelling of 3D hand-object(s) interaction regards both the appearance and the physical state of the scene and is parameterized over the hand motion (26 DoFs) between two successive instants in time. We demonstrate that our approach manages to track the 3D pose of all objects and the 3D pose and articulation of the hand by only searching for the parameters of the hand motion. In the proposed framework, covert scene state is inferred by connecting it to the overt state, through the incorporation of physics. thus, our tracking approach treats a variety of challenging observability issues in a principled manner, without the need to resort to heuristics.

关键词： hand's motion parameters objects Hand three Dimensions physical state actor Joints tracking Heuristics

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