An introduction to Markov logic networks and application in video activity analysis

Cheng, Guangchun; Wan, Yiwen; Buckles, Bill P.; Huang, Yan

doi:10.1109/icccnt.2014.6963049

Cited by 4 publications

(1 citation statement)

References 6 publications

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“…This kind of network is called Interval Algebra Network (IAN). Machine learning and Allen's logic are also combined in [22] using Markov Logic Networks (MLN) [100]. These networks are composed of undirected graphs and a set of potential functions.…”

Section: Temporal Logic and Events Managementmentioning

confidence: 99%

MIFTel: a multimodal interactive framework based on temporal logic rules

Avola

Cinque

Bimbo

et al. 2020

Multimed Tools Appl

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Human-computer and multimodal interaction are increasingly used in everyday life. Machines are able to get more from the surrounding world, assisting humans in different application areas. In this context, the correct processing and management of signals provided by the environments is determinant for structuring the data. Different sources and acquisition times can be exploited for improving recognition results. On the basis of these assumptions, we are proposing a multimodal system that exploits Allen's temporal logic combined with a prevision method. The main object is to correlate user's events with system's reactions. After post-elaborating coming data from different signal sources (RGB images, depth maps, sounds, proximity sensors, etc.), the system is managing the correlations between recognition/detection results and events in real-time to create an interactive environment for the user. For increasing the recognition reliability, a predictive model is also associated with the proposed method. The modularity of the system grants a full dynamic development and upgrade with custom modules. Finally, a comparison with other similar systems is shown, underlining the high flexibility and robustness of the proposed event management method. Ringraziamenti Questo percorso di crescita personale ha portato ad interiorizzare conoscenze notevoli in ambito della computer vision e non solo. Devo molto al Prof. Luigi Cinque per questa opportunità offertami. Inoltre, la sua umanità è stata la virtù più rara che abbia trasmesso. Contestualmente, ho avuto il piacere e l'onore di poter lavorare con un gruppo di ricerca tra i migliori d'Italia. Il Visionlab è diventato un punto di riferimento in questi 3 anni e senza Daniele, Cristiano e Marco probabilmente non avrei raggiunto i risultati di oggi. Devo molto anche a loro. Infine, devo fare un ringraziamento speciale a Danilo Avola, colui che ha fornito un supporto determinante alla ricerca dell'intero laboratorio. Una guida, un maestro ed anche un amico.

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Section: Temporal Logic and Events Managementmentioning

confidence: 99%

MIFTel: a multimodal interactive framework based on temporal logic rules

Avola

Cinque

Bimbo

et al. 2020

Multimed Tools Appl

View full text Add to dashboard Cite

show abstract

On-Line Inference Comparison with Markov Logic Network Engines for Activity Recognition in AAL Environments

Fernández-Carmona

Bellotto

2016

2016 12th International Conference on Intelligent Environments (IE)

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Models for Music Analysis From a Markov Logic Networks Perspective

Papadopoulos¹,

Tzanetakis²

2017

IEEE/ACM Trans. Audio Speech Lang. Process.

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Analyzing and formalizing the intricate mechanisms of music is a very challenging goal for Artificial Intelligence. Dealing with real audio recordings requires the ability to handle both uncertainty and complex relational structure at multiple levels of representation. Until now, these two aspects have been generally treated separately, probability being the standard way to represent uncertainty in knowledge, while logical representation being the standard way to represent knowledge and complex relational information. Several approaches attempting a unification of logic and probability have recently been proposed. In particular Markov Logic Networks (MLNs) which combine first-order logic and probabilistic graphical models have attracted increasing attention in recent years in many domains. This paper introduces MLNs as a highly flexible and expressive formalism for the analysis of music that encompasses most of the commonly used probabilistic and logic-based models. We first review and discuss existing approaches for music analysis. We then introduce MLNs in the context of music signal processing by providing a deep understanding of how they specifically relate to traditional models, specifically Hidden Markov Models and Conditional Random Fields. We then present a detailed application of MLNs for tonal harmony music analysis that illustrates the potential of this framework for music processing.

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An introduction to Markov logic networks and application in video activity analysis

Cited by 4 publications

References 6 publications

MIFTel: a multimodal interactive framework based on temporal logic rules

MIFTel: a multimodal interactive framework based on temporal logic rules

On-Line Inference Comparison with Markov Logic Network Engines for Activity Recognition in AAL Environments

Models for Music Analysis From a Markov Logic Networks Perspective

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