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An Evidence-Driven Probabilistic Inference Framework for Semantic Image Understanding [chapter]

Spiros Nikolopoulos, Georgios Th. Papadopoulos, Ioannis Kompatsiaris, Ioannis Patras
2009 Lecture Notes in Computer Science  
This work presents an image analysis framework driven by emerging evidence and constrained by the semantics expressed in an ontology.  ...  Our work is an attempt to imitate this behavior by devising an evidence driven probabilistic inference framework using ontologies and bayesian networks.  ...  An example of evidence-driven probabilistic inference for image categorization framework).  ... 
doi:10.1007/978-3-642-03070-3_40 fatcat:vryt24d42rdwvoz5ady7qgekue

Image Interpretation by Combining Ontologies and Bayesian Networks [chapter]

Spiros Nikolopoulos, Georgios Th. Papadopoulos, Ioannis Kompatsiaris, Ioannis Patras
2012 Lecture Notes in Computer Science  
A bayesian network (BN) is used for integrating statistical and explicit knowledge and perform hypothesis testing using evidence-driven probabilistic inference.  ...  In this work we propose a framework that performs knowledge-assisted analysis of visual content using ontologies to model domain knowledge and conditional probabilities to model the application context  ...  In the same lines, [4] proposes a framework for semantic image understanding that integrates in the same knowledge-based inference framework (based on BNs), both low-level and semantic features.  ... 
doi:10.1007/978-3-642-30448-4_39 fatcat:yrbi3oyxvncj7hpyeoeszvegne

Evidence-Driven Image Interpretation by Combining Implicit and Explicit Knowledge in a Bayesian Network

S. Nikolopoulos, G. T. Papadopoulos, I. Kompatsiaris, I. Patras
2011 IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)  
Then, a Bayesian network is used for integrating statistical and explicit knowledge and performing hypothesis testing using evidence-driven probabilistic inference.  ...  Index Terms-Bayesian networks (BNs), Focus of attention (FoA), knowledge-assisted image analysis, ontologies, probabilistic inference.  ...  Evidence-Driven Probabilistic Inference To accommodate for evidence-driven probabilistic inference, our framework uses a BN derived from the domain ontology.  ... 
doi:10.1109/tsmcb.2011.2147781 pmid:21642042 fatcat:jzryxe4ugfbdvipyx7lcrju56u

Compound Document Analysis by Fusing Evidence Across Media

Spiros Nikolopoulos, Christina Lakka, Ioannis Kompatsiaris, Christos Varytimidis, Konstantinos Rapantzikos, Yannis Avrithis
2009 2009 Seventh International Workshop on Content-Based Multimedia Indexing  
probabilistic inference on a bayesian network that incorporates knowledge about the domain.  ...  In this paper a cross media analysis scheme for the semantic interpretation of compound documents is presented.  ...  Discussion & future work In this paper we show how an evidence driven probabilistic inference framework that incorporates domain knowledge, can be used to facilitate cross media analysis of compound documents  ... 
doi:10.1109/cbmi.2009.35 dblp:conf/cbmi/NikolopoulosLKVRA09 fatcat:ysg3uw5zuzbn3ejop3ljh7jymq

Plants Don't Walk on the Street: Common-Sense Reasoning for Reliable Semantic Segmentation [article]

Linara Adilova, Elena Schulz, Maram Akila, Sebastian Houben, Jan David Schneider, Fabian Hueger, Tim Wirtz
2021 arXiv   pre-print
We present an initial study adapting the well-established Probabilistic Soft Logic (PSL) framework to validate and improve on the problem of semantic segmentation.  ...  However, learning common knowledge only from data is hard and approaches for knowledge integration are an active research area.  ...  The work of L.A. was supported by the Fraunhofer Center for Machine Learning within the Fraunhofer Cluster for Cognitive Internet Technologies.  ... 
arXiv:2104.09254v1 fatcat:udu4x6hqyrg5bbowkkyvrzvlpy

Semantic Event Fusion of Different Visual Modality Concepts for Activity Recognition

Carlos F. Crispim-Junior, Vincent Buso, Konstantinos Avgerinakis, Georgios Meditskos, Alexia Briassouli, Jenny Benois-Pineau, Ioannis Yiannis Kompatsiaris, Francois Bremond
2016 IEEE Transactions on Pattern Analysis and Machine Intelligence  
This paper proposes a hybrid framework between knowledge-driven and probabilistic-driven methods for event representation and recognition.  ...  the framework with a mechanism to handle noisy and ambiguous concept observations, an ability that most knowledge-driven methods lack.  ...  CONCLUSION This paper introduced a framework for semantic event fusion, composed of a novel probabilistic, knowledge-driven framework for event representation and recognition, and a novel algorithm for  ... 
doi:10.1109/tpami.2016.2537323 pmid:26955015 fatcat:b3v4qtxjtvg23lljhqy4lma6om

Semantic-level decentralized multi-robot decision-making using probabilistic macro-observations

Shayegan Omidshafiei, Shih-Yuan Liu, Michael Everett, Brett T. Lopez, Christopher Amato, Miao Liu, Jonathan P. How, John Vian
2017 2017 IEEE International Conference on Robotics and Automation (ICRA)  
This is important not only for low-level observations (e.g., accelerometer data), but also for high-level observations such as semantic object labels.  ...  Classification accuracy of the proposed macro-observation scheme, called Hierarchical Bayesian Noise Inference (HBNI), is shown to exceed existing methods.  ...  It takes equal amount of evidence for a class to "cancel out" evidence against it, an issue encountered in Bayesbased occupancy mapping [16] .  ... 
doi:10.1109/icra.2017.7989107 dblp:conf/icra/OmidshafieiLELA17 fatcat:l4pxld4rjjbc5kbwqtg5fbcncy

A Review on Intelligent Object Perception Methods Combining Knowledge-based Reasoning and Machine Learning [article]

Filippos Gouidis, Alexandros Vassiliades, Theodore Patkos, Antonis Argyros, Nick Bassiliades, Dimitris Plexousakis
2020 arXiv   pre-print
The algorithm uses a CNN-based framework tailored for object detection and combines it with a graphical model designed for the inference of object states.  ...  Similarly, Lu et al. (2016) exploit language priors extracted from the semantic features of an image, in order to facilitate the understanding of visual relationships.  ... 
arXiv:1912.11861v2 fatcat:dhjvffblprbonn4xtssj3jzb6q

Large-scale semantic mapping and reasoning with heterogeneous modalities

Andrzej Pronobis, Patric Jensfelt
2012 2012 IEEE International Conference on Robotics and Automation  
This paper presents a probabilistic framework combining heterogeneous, uncertain, information such as object observations, shape, size, appearance of rooms and human input for semantic mapping.  ...  It relies on the concept of spatial properties which make the semantic map more descriptive, and the system more scalable and better adapted for human interaction.  ...  Moreover, the values of all properties for which direct evidence is not available can be inferred based on all the available semantic information.  ... 
doi:10.1109/icra.2012.6224637 dblp:conf/icra/PronobisJ12 fatcat:lltanixi2rhj7jr56ow7yev5si

Visual-Semantic Scene Understanding by Sharing Labels in a Context Network [article]

Ishani Chakraborty, Ahmed Elgammal
2013 arXiv   pre-print
For inference, we derive an iterative Data Augmentation algorithm that pools the label probabilities and maximizes the joint label posterior of an image.  ...  To this end, we present the Visual Semantic Integration Model (VSIM) that represents object labels as entities shared between semantic and visual contexts and infers a new image by updating labels through  ...  ., the goal of VSIM is to infer the semantic object labels in an image, given its appearance features.  ... 
arXiv:1309.3809v1 fatcat:vvl4yu6ppjae5lohlc4slbmqwe

Ontological inference for image and video analysis

Christopher Town
2006 Machine Vision and Applications  
This paper presents an approach to designing and implementing extensible computational models for perceiving systems based on a knowledge-driven joint inference approach.  ...  Queries are parsed using a probabilistic grammar and Bayesian networks to map high level concepts onto low level image descriptors, thereby bridging the "semantic gap" between users and the retrieval system  ...  Acknowledgements The author would like to acknowledge financial support from AT&T Labs Research and the Royal Commission for the Exhibition of 1851.  ... 
doi:10.1007/s00138-006-0017-3 fatcat:hat7556tmzbctpebyvzx3xr4dy

Scene understanding by labeling pixels

Stephen Gould, Xuming He
2014 Communications of the ACM  
Figure 7 . 7 Example semantic segmentation for an image from the MSRC dataset.  ...  understanding is to label every pixel in an image with a category label using probabilistic models known as CRFs that can handle uncertainty and propagate contextual information across the image. ˽ Improved  ...  Xuming He (xuming.he@nicta.com.au) is a senior researcher in the Computer Vision Research Group at the National ICT Australia, Canberra, and an adjunct fellow (senior lecturer) in the Research School of  ... 
doi:10.1145/2629637 fatcat:shdt7ewirvgtth3k5jcwyl2n6q

A Bayesian network modeling approach for cross media analysis

Christina Lakka, Spiros Nikolopoulos, Christos Varytimidis, Ioannis Kompatsiaris
2011 Signal processing. Image communication  
Existing methods for the semantic analysis of multimedia, although effective for single-medium scenarios, are inherently flawed in cases where knowledge is spread over different media types.  ...  More specifically, our contribution is on proposing a modeling approach for Bayesian Networks that defines this conceptual space and allows evidence originating from the domain knowledge, the application  ...  Evidence-driven probabilistic inference In order to perform evidence driven probabilistic inference on the constructed BN we rely on message passing algorithms.  ... 
doi:10.1016/j.image.2011.01.004 fatcat:5mlr54qminazxls6f4umnuj3xm

Complex Events Recognition under Uncertainty in a Sensor Network [article]

Atul Kanaujia, Tae Eun Choe, Hongli Deng
2014 arXiv   pre-print
meaningful information for high level inference.  ...  While state-of-the-art vision techniques exist in detecting visual entities (humans, vehicles and scene elements) in an image, a missing functionality is the ability to merge the information to reveal  ...  To this end, unlike past MLN based activity recognition frameworks [28, 14, 17] ) that used hard evidences, we generate predicates with an associated probability(soft evidence).  ... 
arXiv:1411.0085v1 fatcat:hmihv4za7bfrbeeiuvsuhebznq

Table-Top Scene Analysis Using Knowledge-Supervised MCMC [article]

Ziyuan Liu, Dong Chen, Kai M. Wurm, Georg von Wichert
2020 arXiv   pre-print
In this paper, we propose a probabilistic method to generate abstract scene graphs for table-top scenes from 6D object pose estimates.  ...  Our approach to generate scene graphs is probabilistic: Uncertainty in the object poses is addressed by a probabilistic sensor model that is embedded in a data driven MCMC process.  ...  Acknowledgements This work is accomplished with the support of the Technische Universität München -Institute for Advanced Study, funded by the German Excellence Initiative.  ... 
arXiv:2002.08417v1 fatcat:yvkcyphwp5cqtf4ddg6wha73yi
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