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Author Index

2010 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition  
Liu, Xu Demo: Mobile Visual Search, Linking Printed Documents to Digital Media Liu, Yan Large-Scale Image Retrieval with Compressed Fisher Vectors Large-Scale Image Categorization with Explicit  ...  Large-Scale Weakly-Tagged Image Databases from the Web Garcia, Ricardo R.  ... 
doi:10.1109/cvpr.2010.5539913 fatcat:y6m5knstrzfyfin6jzusc42p54

Nonparametric Bayesian Learning of Infinite Multivariate Generalized Normal Mixture Models and Its Applications

Sami Bourouis, Roobaea Alroobaea, Saeed Rubaiee, Murad Andejany, Nizar Bouguila
2021 Applied Sciences  
The statistical mixture is learned via a nonparametric MCMC-based Bayesian approach in order to avoid the crucial problem of model over-fitting and to allow uncertainty in the number of mixture components  ...  and human activity categorization.  ...  Conclusions We have proposed in this paper an effective hierarchical nonparametric statistical Bayesian framework that tackles complex data modeling, categorization and classification.  ... 
doi:10.3390/app11135798 fatcat:6jclwgyu2zhkbavyu4xfxpm63q

Object Categorization by Compositional Graphical Models [chapter]

Björn Ommer, Joachim M. Buhmann
2005 Lecture Notes in Computer Science  
We propose a sparse image representation based on localized feature histograms of salient regions.  ...  This contribution proposes a compositionality architecture for visual object categorization, i.e., learning and recognizing multiple visual object classes in unsegmented, cluttered real-world scenes.  ...  Therefore the part representations, the compositions, as well as the overall image categorization are all combined in a single graphical model, a Bayesian network [19] .  ... 
doi:10.1007/11585978_16 fatcat:354bcdvvrfhsxbtrzxzdggq3u4

Collapsed Variational Inference for Nonparametric Bayesian Group Factor Analysis [article]

Sikun Yang, Heinz Koeppl
2018 arXiv   pre-print
In this paper we present an efficient collapsed variational inference (CVI) algorithm for the nonparametric Bayesian group factor analysis (NGFA) model built upon an hierarchical beta Bernoulli process  ...  To date, most available GFA models require Gibbs sampling or slice sampling to perform inference, which prevents the practical application of GFA to large-scale data.  ...  poorly for large-scale GFA problems.  ... 
arXiv:1809.03566v2 fatcat:45bv42unoffwrhjndw27dhgcbu

Learning Multiscale Representations of Natural Scenes Using Dirichlet Processes

Jyri J. Kivinen, Erik B. Sudderth, Michael I. Jordan
2007 2007 IEEE 11th International Conference on Computer Vision  
We develop nonparametric Bayesian models for multiscale representations of images depicting natural scene categories.  ...  We show that our generative models capture interesting qualitative structure in natural scenes, and more accurately categorize novel images than models which ignore spatial relationships among features  ...  In this paper, we develop nonparametric statistical methods which learn multiscale representations of natural scenes, and use these models to accurately categorize images of new environments.  ... 
doi:10.1109/iccv.2007.4408870 dblp:conf/iccv/KivinenSJ07 fatcat:e5k3pvkspbcozitnkwfsnljuny

Collapsed Variational Inference for Nonparametric Bayesian Group Factor Analysis

Sikun Yang, Heinz Koeppl
2018 Zenodo  
In this paper we present an efficient collapsed variational inference (CVI) algorithm for the nonparametric Bayesian group factor analysis (NGFA) model built upon an hierarchical beta Bernoulli process  ...  To date, most available GFA models require Gibbs sampling or slice sampling to perform inference, which prevents the practical application of GFA to large-scale data.  ...  poorly for large-scale GFA problems.  ... 
doi:10.5281/zenodo.1966177 fatcat:aphfvpgui5gqdjdlu5kbvbfhoe

Bayesian crack detection in ultra high resolution multimodal images of paintings

Bruno Cornelis, Yun Yang, Joshua T. Vogelstein, Ann Dooms, Ingrid Daubechies, David Dunson
2013 2013 18th International Conference on Digital Signal Processing (DSP)  
Thanks to recent developments in digital acquisition techniques, powerful image analysis algorithms are developed which can be useful non-invasive tools to assist in the restoration and preservation of  ...  The proposed Bayesian classifier, which we will refer to as conditional Bayesian tensor factorization or CBTF, is assessed by visually comparing classification results with the Random Forest (RF) algorithm  ...  MCA constructs a sparse representation of a signal or an image considering that it is a combination of features which are sparsely represented by different dictionaries.  ... 
doi:10.1109/icdsp.2013.6622710 dblp:conf/icdsp/CornelisYVDDD13 fatcat:cqdy3h2cevax5mtedjz2q2d6by

Bayesian crack detection in ultra high resolution multimodal images of paintings [article]

Bruno Cornelis, Yun Yang, Joshua T. Vogelstein, Ann Dooms, Ingrid Daubechies, David Dunson
2013 arXiv   pre-print
Thanks to recent developments in digital acquisition techniques, powerful image analysis algorithms are developed which can be useful non-invasive tools to assist in the restoration and preservation of  ...  The proposed Bayesian classifier, which we will refer to as conditional Bayesian tensor factorization or CBTF, is assessed by visually comparing classification results with the Random Forest (RF) algorithm  ...  MCA constructs a sparse representation of a signal or an image considering that it is a combination of features which are sparsely represented by different dictionaries.  ... 
arXiv:1304.5894v2 fatcat:dhpbslom65etzkz2vcuqvm4mmq

CrossCat: A Fully Bayesian Nonparametric Method for Analyzing Heterogeneous, High Dimensional Data [article]

Vikash Mansinghka, Patrick Shafto, Eric Jonas, Cap Petschulat, Max Gasner, Joshua B. Tenenbaum
2015 arXiv   pre-print
CrossCat is based on approximately Bayesian inference in a hierarchical, nonparamet- ric model for data tables.  ...  CrossCat infers multiple non-overlapping views of the data, each consisting of a subset of the variables, and uses a separate nonparametric mixture to model each view.  ...  Several authors were involved in the engineering of multiple high-performance commercial implementations.  ... 
arXiv:1512.01272v1 fatcat:a4mut4sw4bawjftjbhtignmc2q

Pattern recognition: Historical perspective and future directions

Azriel Rosenfeld, Harry Wechsler
2000 International journal of imaging systems and technology (Print)  
In this framework, data representation requires the specification of a basis set of approximating functions.  ...  The sections of this paper deal with the categorization and functional approximation problems; the four components of a pattern recognition system; and trends in predictive learning, feature selection  ...  , and range of applications using comparative trials on large-scale commercial and industrial problems.  ... 
doi:10.1002/1098-1098(2000)11:2<101::aid-ima1>3.0.co;2-j fatcat:7avx2v64l5h6leyn5pdgg6s4ey

2021 Index IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 43

2022 IEEE Transactions on Pattern Analysis and Machine Intelligence  
-that appeared in this periodical during 2021, and items from previous years that were commented upon or corrected in 2021.  ...  Note that the item title is found only under the primary entry in the Author Index.  ...  Slavcheva, M., +, TPAMI Aug. 2021 2838-2850 Large-scale systems Accelerated Variance Reduction Stochastic ADMM for Large-Scale Machine Learning.  ... 
doi:10.1109/tpami.2021.3126216 fatcat:h6bdbf2tdngefjgj76cudpoyia

Image Categorization Using Hierarchical Spatial Matching Kernel

Tam T. Le, Yousun Kang, Akihiro Sugimoto
2013 The Journal of the Institute of Image Electronics Engineers of Japan  
In experiments, results of HSMK outperformed those of SPM and led to state-of-the-art performance on several well-known databases of benchmarks in image categorization, even though we use only a single  ...  Spatial pyramid matching (SPM) has been an important approach to image categorization.  ...  Acknowledgements This work was supported in part by JST, CREST, and JSPS. The Journal of the Institute of Image Electronics Engineers of Japan Vol.42 No. 2 (2013)  ... 
doi:10.11371/iieej.42.214 fatcat:bxsg3kgkbzgidhglncqkgeddvu

An Overview of Bayesian Methods for Neural Spike Train Analysis

Zhe Chen
2013 Computational Intelligence and Neuroscience  
With the advancement of multielectrode recording and imaging technologies, it has become increasingly demanding to develop statistical tools for analyzing large neuronal ensemble spike activity.  ...  Here we present a tutorial overview of Bayesian methods and their representative applications in neural spike train analysis, at both single neuron and population levels.  ...  This work was also supported by the NSF-IIS CRCNS (Collaborative Research in Computational Neuroscience) Grant (no. 1307645) from the National Science Foundation.  ... 
doi:10.1155/2013/251905 pmid:24348527 pmcid:PMC3855941 fatcat:nkst6mt3sfcqheuxheda3wq4wq

A Bayesian nonparametric approach to super-resolution single-molecule localization

Mariano I. Gabitto, Herve Marie-Nelly, Ari Pakman, Andras Pataki, Xavier Darzacq, Michael I. Jordan
2021 Annals of Applied Statistics  
To address these problems, we present a Bayesian nonparametric framework capable of identifying individual emitting molecules in super-resolved time series.  ...  SRM resolves photoswitchable fluorophores in a field of view by sparsely and randomly activating individual light emitters and then localizing them with subdiffraction precision (Figure 1B ).  ...  We have presented a Bayesian nonparametric method for the identification of fluorescent molecules in super-resolution experiments.  ... 
doi:10.1214/21-aoas1441 fatcat:qodke52ipjbr5bn3a5qzmhr5d4

The Future of Data Analysis in the Neurosciences [article]

Danilo Bzdok, B. T. Thomas Yeo
2016 arXiv   pre-print
We believe that large-scale data analysis will use more models that are non-parametric, generative, mixing frequentist and Bayesian aspects, and grounded in different statistical inferences.  ...  In the last 10 years neuroscience spawned quantitative big-sample datasets on microanatomy, synaptic connections, optogenetic brain-behavior assays, and high-level cognition.  ...  In a nutshell, neuroscience is entering the era of large-scale data collection, curation, and collaboration with a pressing need for statistical models tailored for high-dimensional inference.  ... 
arXiv:1608.03465v1 fatcat:roen4d2axncufftj3ifjjimqpe
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