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HDPA: HIERARCHICAL DEEP PROBABILITY ANALYSIS FOR SCENE PARSING

HDPA: HIERARCHICAL DEEP PROBABILITY ANALYSIS FOR SCENE PARSI...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Yuan, Yuan Jiang, Zhiyu Wang, Qi Chinese Acad Sci Xian Inst Opt & Precis Mech State Key Lab Transient Opt & Photon Ctr OPT IMagery Anal & Learning OPTIMAL Xian 710119 Shaanxi Peoples R China Univ Chinese Acad Sci 19A Yuquanlu Beijing 100049 Peoples R China Northwestern Polytech Univ Sch Comp Sci Xian 710072 Shaanxi Peoples R China Northwestern Polytech Univ Ctr OPT IMagery Anal & Learning OPTIMAL Xian 710072 Shaanxi Peoples R China

ISBN: (纸本)9781509060672

Scene parsing is an important task in computer vision and many issues still need to be solved. One problem is about the non-unified framework for predicting things and stuff and the other one refers to the inadequate description of contextual information. In this paper, we address these issues by proposing a Hierarchical Deep Probability Analysis(HDPA) method which particularly exploits the power of probabilistic graphical model and deep convolutional neural network on pixel-level scene parsing. To be specific, an input image is initially segmented and represented through a CNN framework under Gaussian pyramid. Then the graphical models are built under each scale and the labels are ultimately predicted by structural analysis. Three contributions are claimed: unified framework for scene labeling, hierarchical probabilistic graphical modeling and adequate contextual information consideration. Experiments on three benchmarks show that the proposed method outperforms the state-of-the-arts in scene parsing.

关键词： Computer vision scene parsing semantic segmentation probabilistic graphical model CRF

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An Unsupervised Framework for Author-Paper Linking in Bibliographic Retrieval System 14

An Unsupervised Framework for Author-Paper Linking in Biblio...

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14th International Conference on Semantics, Knowledge and Grids (SKG)

作者： Ding, Xin Zhang, Hui Guo, Xiaoyu Beihang Univ Sch Comp Sci & Engn Beijing 100191 Peoples R China

ISBN: (纸本)9781728104416

Author name ambiguity can significantly impact the accuracy of a bibliographic retrieval system, especially when author name served as a search keyword. In this paper, we propose an unsupervised approach addressing the name ambiguity problem by linking papers to their corresponding authors based on clustering result of word embeddings. Each cluster represents a collection of words in a certain research area. Papers and authors which to be disambiguated are then assigned a probability of each research area they belong to. We put those probabilities and some metadata of papers and authors as features into a graphic model and do the collective inference. Experiment shows that our entirely unsupervised method perform well for a Chinese Bibliographic Retrieval System even with a huge amount of noisy in its database.

关键词： author paper linking name disambiguation probabilistic graphical model clustering

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Recognizing Point Clouds using Conditional Random Fields 22

Recognizing Point Clouds using Conditional Random Fields

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22nd International Conference on Pattern Recognition (ICPR)

作者： Husain, Farzad Dellen, Babette Torras, Carme UPC CSIC Inst Robot & Informat Ind Barcelona 08028 Spain RheinAhrCampus Hsch Koblenz D-53424 Remagen Germany

ISBN: (纸本)9781479952083

Detecting objects in cluttered scenes is a necessary step for many robotic tasks and facilitates the interaction of the robot with its environment. Because of the availability of efficient 3D sensing devices as the Kinect, methods for the recognition of objects in 3D point clouds have gained importance during the last years. In this paper, we propose a new supervised learning approach for the recognition of objects from 3D point clouds using Conditional Random Fields, a type of discriminative, undirected probabilistic graphical model. The various features and contextual relations of the objects are described by the potential functions in the graph. Our method allows for learning and inference from unorganized point clouds of arbitrary sizes and shows significant benefit in terms of computational speed during prediction when compared to a state-of-the-art approach based on constrained optimization.

关键词： interactive devices learning (artificial intelligence) mobile robots object detection object recognition optimisation probability robot vision 3D sensing devices Kinect conditional random fields constrained optimization point cloud recognition probabilistic graphical model robotic tasks supervised learning graphical models Laser radar Mathematical model Object recognition Three-dimensional displays Training Vectors

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STEREOPHONIC SPECTROGRAM SEGMENTATION USING MARKOV RANDOM FIELDS

STEREOPHONIC SPECTROGRAM SEGMENTATION USING MARKOV RANDOM FI...

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22nd IEEE International Workshop on Machine Learning for Signal Processing (MLSP)

作者： Kim, Minje Smaragdis, Paris Ko, Glenn G. Rutenbar, Rob A. Univ Illinois Dept Comp Sci Urbana IL USA Univ Illinois Dept Elect & Comp Engn Urbana IL USA Adobe Syst Inc Urbana IL USA

ISBN: (纸本)9781467310260

There is a good amount of similarity between source separation approaches that use spectrograms captured from multiple microphones and computer vision algorithms that use multiple images for segmentation problems. Just as one would use Markov random fields (MRF) to solve image segmentation problems, we propose a method of modeling source separation using MRFs, and then solving such problems via common MRF inference methods. To this end, as a preprocessing, we convert stereophonic spectrograms into a integrated form based on their inter-channel level differences (ILD), which is a procedure analogous to getting a disparity map from stereo images for matching problems. Given the ILD matrix as an observed image, we estimate latent labels which stand for the responsibility of each spectrogram's time/frequency bin to a specific sound source. It is shown that the proposed method shows reasonable separation performance in a variety of mixing environments including online separation and moving sources. We expect this new way of formulating source separation problems to help exploit advantages of probabilistic graphical models and the recent advances in low-power, high-performance hardware suited for such tasks.

关键词： Blind Source Separation Markov Random Fields probabilistic graphical model Gibbs Sampling

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Simulator Prototyping Through graphical Dependency modeling 12th

Simulator Prototyping Through Graphical Dependency Modeling

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12th International Conference on Machine Learning and Data Mining (MLDM)

作者： Dheenadayalan, Kumar Muralidhara, V. N. Srinivasaraghavan, G. Int Inst Informat Technol Bangalore Karnataka India

ISBN: (纸本)9783319419206;9783319419190

Given a complex system, simulating the behavior of the system under various conditions and inputs is a common requirement in different domains. Designing a simulator by non-domain expert is not an easy task. However, the vast amount of performance related parameters that can be monitored on any physical system can help in probabilistic modeling of the system. Accurate modeling of a complex system requires the identification of dependency among the individual components. These dependencies can be deduced by probabilistic analysis of the performance parameters generated by the physical system. We propose constrained Hill Climbing for structure learning of a Bayesian Network to learn the dependency among internal components of the system. Dependency graph, thus generated will act as a basis for developing a prototype simulator. Multivariate Adaptive Regression Splines are compared with the proposed constrained hill climbing to deduce the mathematical relation between the interdependent components. Two prototype simulators are designed, one for a complex large-scale storage system and another for a group of computer servers. Behavior of both the physical systems is tested on workload traces. The normalized mean absolute error for simulation of storage systems and server was 4.6% & 3.75% respectively. Results indicate that the proposed simulation prototyping can be a useful and unique way of understanding complex systems without the help of a domain expert.

关键词： probabilistic graphical model Bayesian network Regression Multivariate adaptive regression splines Simulator

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DATA FUSION FOR BUILDING RECONSTRUCTION FROM MULTI-ASPECT INSAR DATA

DATA FUSION FOR BUILDING RECONSTRUCTION FROM MULTI-ASPECT IN...

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International-Society-for-Photogrammetry-and-Remote-Sensing Hannover Workshop

作者： Maksymiuk, O. Stilla, U. Tech Univ Munich Photogrammetry & Remote Sensing D-80333 Munich Germany

ISBN: (纸本)9781629935201

In this paper we present a novel approach for 3D reconstruction of point clouds based on single baseline Multi-Aspect InSAR (MASAR) data. The point clouds represent an intermediate result to achieve a comprehensive building reconstruction framework. The exact determination of scatterers based on SAR data is a non-trivial task since the optimal solution requires the knowledge of the number of scatterers within one range cell. In recent years many methods were proposed addressing this problem but most of them require multiple observations making them inapplicable to our task. We use a probabilistic graphical model (PGM) to combine all aspects into a common framework exploiting all contradictions and redundancy in the data. The model is used iteratively together with local optimizations adjusting the hypothesis of the scatterer within one range cell to the corresponding observation. This makes it possible to find a global solution.

关键词： InSAR Multi-Aspect MASAR 3D Reconstruction Building Reconstruction Urban Areas probabilistic graphical model Data Fusion Radargrammetry Compressive Sensing Sparsity

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A CONCEPT FOR BUILDING RECONSTRUCTION FROM AIRBORNE MULTI-ASPECT SAR DATA

A CONCEPT FOR BUILDING RECONSTRUCTION FROM AIRBORNE MULTI-AS...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Maksymiuk, Oliver Stilla, Uwe Tech Univ Munich D-80290 Munich Germany

ISBN: (纸本)9781467311595

In this work we present a new concept towards building reconstruction based on multi-aspect SAR (MASAR) data where we make use of the inherent redundancy and contradiction of MASAR data. Instead of treating every aspect seperately we perform a joint optimization on all available data. For this purpose we use probabilistic graphical model (PGM) as a declarative representation to encode our knowledge and to reconstruct the most likely objects in the scene.

关键词： Building Reconstruction Multi-Aspect SAR Interferometry Radargrammetry probabilistic graphical model Conditional Random Field

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Parallel Compact Roadmap Construction of 3D Virtual Environments on the GPU

Parallel Compact Roadmap Construction of 3D Virtual Environm...

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IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Bleiweiss, Avi Architecture Group NVIDIA Corporation Santa Clara CA 95050 United States

ISBN: (纸本)9781424466757

The representation of probabilistic graphical model often encodes a network whose size is unboundedly large. Such networks pose particular challenges to inference algorithms, specifically making the task of robot path queries highly inefficient due to poor locality of memory references. Whereas a more predictable, resolution complete method yields a highly compact graph structure that captures much of the signal in distributing the configuration free space. In this paper we demonstrate an efficient data parallel algorithm for mapping the computationally intensive, Reachability Roadmap method on the GPU. For our implementation on the recently introduced NVIDIA's Fermi architecture, we show roadmap construction time under twenty seconds for a closure resolution of 55x55x55 cells. Moving forward, our system is well positioned to address smooth navigation of robots in a dynamically changing 3D virtual environment.

关键词： 3D virtual environment GPU NVIDIA Fermi architecture computer graphic equipment coprocessors data parallel algorithm graphical representation inference algorithm mobile robots parallel algorithms path planning probabilistic graphical model probability query processing reachability roadmap method resolution complete method roadmap construction robot path query solid modelling user interfaces virtual reality

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An Approach to Sensitivity Analysis of Inference Equations in Algebraic Bayesian Networks 2nd

An Approach to Sensitivity Analysis of Inference Equations i...

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2nd International Scientific Conference on Intelligent Information Technologies for Industry (IITI)

作者： Zolotin, Andrey A. Malchevskaya, Ekaterina A. Tulupyev, Alexander L. Sirotkin, Alexander V. St Petersburg State Univ St Petersburg Russia Russian Acad Sci St Petersburg Inst Informat & Automat 3914 Th Line Vasilyevsky Ostrov St Petersburg 199178 Russia

ISBN: (纸本)9783319683218

An approach to the sensitivity analysis of local a posterior inference equations in algebraic Bayesian networks is proposed in the paper. Performed a sensitivity analysis of first a posterior inference task for stochastic and deterministic evidences propagated into the knowledge pattern with scalar estimates. For each of the considered cases the necessary metrics are chosen and transformations are carried out, that result into a linear programming problem. In addition, for each type of evidence theorems that postulate upper sensitivity estimates are formulated and proofs are provided. Theoretical results are implemented in CSharp using the module of probabilistic-logical inference software complex. A series of computational experiments is conducted. The results of experiments are visualized using tables and charts. The proposed visualization demonstrates the high sensitivity of the considered models, that confirms the correctness of their use.

关键词： probabilistic graphical model Sensitivity statistical estimate Machine learning Posterior inference Evidence propagation Algebraic Bayesian Network Matrix-vector equations

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Proposed efficient approach for Classification for Multi-Relational Data Mining using Bayesian Belief Network

Proposed efficient approach for Classification for Multi-Rel...

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International Conference on Green Computing Communication and Electrical Engineering (ICGCCEE)

作者： Bharwad, Nileshkumar D. Goswami, Mukesh M. Dharmsinh Desai Univ Fac Technol Dept Informat Technol Nadiad India

ISBN: (纸本)9781479949816

Multi-Relational Data Mining is an active area of research for researchers from last many decades. Relational database is an important source of structure data, hence richest source of knowledge. Most of the commercial and application oriented data uses relational database system in which multiple relations are link through primary key, foreign key relationship. Thus, the field of Multi-Relational Data Mining (MRDM) deals with extraction of information from relational database containing multiple tables related with each other. In order to extract important information or knowledge, it is required to apply Data Mining algorithms on this relational database but most of these algorithms works only on single table. Generating a single table may result in to loss of important information, like relation between tuples. Also it is a not efficient in terms of time and space. In this research, we propose a probabilistic graphical model, namely Bayesian Belief Network (BBN), based approach that considers not only attributes of table but also the relation between tables. The conditional dependencies between tables is derived from Semantic Relationship Graph (SRG) of the relational database. This research also aims, to find relevant attributes from Multi-Relational dataset in order to improve the accuracy.

关键词： Multi-Relational Data Mining Relational database Bayesian Belief Network Data Mining probabilistic graphical model Semantic Relationship Graph

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