Extending MOWL for Event Representation (E-MOWL)

Detecting and Correlating Video-Based Event Patterns: An Ontology Driven Approach

Abstract: With increasing amount of information (video, text) being available today, it has become non-trivial to develop techniques to categorize documents into contextually meaningful classes. The information as available in the documents is composed of sequence of events termed as patterns. It is evident to know the important trends as observed from patterns that are emerging over a specific time period and space. For identifying the patterns, we must focus on semantic meaning of documents. Tracing such patterns in videos or texts manually is a time-consuming, cumbersome or an impossible task. So, in this paper we have devised an unsupervised trend discovery approach that detects and correlates event patterns from videos temporally as well as spatially. We begin by building our own document collection on the basis of contextual meaning of documents. This helps in associating an input video with another video or text documents on the basis of their semantic meaning. This approach helps in accumulating variety of information that is scattered over the web thus providing relatively complete information about the video. The highly correlated words are grouped in a topic using Latent Dirichlet Allocation (LDA). To identify topics an E-MOWL based ontology is used. This event ontology helps in discovering associations and relations between the various events. With this kind of representation, the users can infer different concepts as emerged over time. For identifying the various spatial patterns that exist corresponding to an event in a document, we have utilized geographic ontology (Geoontology). We establish validity of our approach using experimental results.

Ontology driven contextual tagging of multimedia data

Abstract: To exhibit multi-modal information and to facilitate people in finding multimedia resources, tagging plays a significant role. Various public events like protests and demonstrations are always consequences of break out of some public outrage resulting from prolonged exploitation and harassment. This outrage can be seen in news footage, blogs, text news and other web data. So, aggregating this variety of data from heterogeneous sources is a prerequisite step for tagging multimedia data with appropriate content. Since content has no meaning without a context, a video should be tagged with its relevant context and content information to assist user in multimedia retrieval. This paper proposes a model for tagging of multimedia data on the basis of contextual meaning. Since context is knowledge based, it has to be guided and learned by ontology which will help fragmented information to be represented in a more meaningful way. Our tagging approach is novel and has practical applicability in the sense that whenever a new video is uploaded on some media sharing site, the context and content information gets attached automatically to a video. Thus, providing relatively complete information associated with the video.

Digitization of Disaster Management: A Multimedia Ontological Approach

Abstract: In the era of World Wide Web, dependency has been increasing on real-time response, communication, disaster preparedness, and analysis by our society, experts from different areas including paramedics, risk management team, police, and firefighters. To ensure real-time response, appropriate information retrieval, expediting undertaking and response preparation and handling communique with all involved parties, there is a requirement of digitizing the resources, concepts semantically along with exploiting the information embedded in multimedia content. We also know that ontology has been proven the excellent mean of digitization. So through this paper, we propose a multimedia-driven disaster management ontology, featuring a different representation scheme integrating the multimedia content. This will offer semantic analysis and interoperability across heterogeneous multimedia data sources, hence facilitating in real-time disaster management.

An Ontology-Based Architecture for Providing Insights in Wireless Networks Domain

Abstract: Ontology-based approaches have been explored in several domains for knowledge representation and improving accuracy. However, ontology-based approaches for assisting a decision maker by delivering a concrete plan from analyzing the insights extracted from an ontology, have not received much attention. Insights-as-a-service is a technology that aids a decision maker by providing a concrete action plan, involving a comparative analysis of patterns derived from the data and the extraction of insights from such an analysis. In this paper, we propose an ontology-based architecture for mining insights within the Wireless Network Ontology (WNO), an ontology generated for the wireless network domain for delivering better wireless network performance. We present and illustrate: (i) the major components of the architecture together with the algorithms used for summarizing the network performance profiles in the form of rank tables, and (ii) how the insight rules (the action plan) are extracted from these tables. By utilizing the proposed approach, an actionable plan for assisting the decision maker can be obtained as domain knowledge is incorporated in the system. Experimental results on a wireless network dataset show that the proposed model provides an optimal action plan for a wireless network to improve its performance by encoding data-driven rules into the ontology and suggesting changes to its current network configuration.

VERL: an ontology framework for representing and annotating video events

Abstract: The notion of "events" is extremely important in characterizing the contents of video. An event is typically triggered by some kind of change of state captured in the video, such as when an object starts moving. The ability to reason with events is a critical step toward video understanding. This article describes the findings of a recent workshop series that has produced an ontology framework for representing video events-called Video Event Representation Language (VERL) -and a companion annotation framework, called Video Event Markup Language (VEML). One of the key concepts in this work is the modeling of events as composable, whereby complex events are constructed from simpler events by operations such as sequencing, iteration, and alternation. The article presents an extensible event and object ontology expressed in VERL and discusses a detailed example of applying VERL and VEML to the description of a "tailgating" event in surveillance video.

A core ontology on events for representing occurrences in the real world

Abstract: Events are central aspect of many semantic ambient media applications such as surveillance, smart homes, automobiles, and others. Existing models for events typically do not follow a systematic development approach, are conceptually narrow with respect to event features, and their semantics is often ambiguous. This makes the communication between and integration of different event-based components and event-based semantic ambient media applications a challenging task. In this paper, we present the Event-Model-F, a formal model of events based on the foundational ontology DOLCE+DnS Ultralite (DUL). The Event-Model-F provides comprehensive support to represent time and space, objects and persons, mereological, causal, and correlative relationships between events, and different interpretations of the same event. It is developed following a pattern-oriented ontology design approach and can be easily extended by domain specific ontologies. We introduce the design and implementation of an application programming interface that allows for easy integration of the Event-Model-F in arbitrary applications. The use of the Event-Model-F is demonstrated at the example of a socio-technical system of emergency response and implemented in the SemaPlorer+?+ application for creating and sharing event descriptions.

Modeling, detecting, and processing events in multimedia

Abstract: Humans think in terms of events and entities. Events pro-vide a natural abstraction of happenings in the real world.The concept of events has a long history in foundational sci-ences such as philosophy and linguistics. After flrst devel-oping objects-based and entity-based approaches, computerscience research is now addressing the concept of events andbuilding many applications that consider events at least asimportant as objects. Consequently, we flnd many difierentsolutions and approaches for modeling, detecting, and pro-cessing events. In addition, we flnd difierent applicationsthat are based on events and make use of events.

A comprehensive study of visual event computing

Abstract: This paper contains a survey on aspects of visual event computing. We start by presenting events and their classifications, and continue with discussing the problem of capturing events in terms of photographs, videos, etc, as well as the methodologies for event storing and retrieving. Later, we review an extensive set of papers taken from well-known conferences and journals in multiple disciplines. We analyze events, and summarize the procedure of visual event actions. We introduce each component of a visual event computing system, and its computational aspects, we discuss the progress of each component and review its overall status. Finally, we suggest future research trends in event computing and hope to introduce a comprehensive profile of visual event computing to readers.

Specifying spatio temporal relations for multimedia ontologies

Abstract: This paper present a novel framework for formal specification of spatio-temporal relations between media objects using fuzzy membership. We have illustrated its use in multimedia ontologies and have described a reasoning framework for creating media based descriptions of concepts.