Proceedings ArticleDOI
Detecting and Correlating Video-Based Event Patterns: An Ontology Driven Approach
Nisha Pahal,Santanu Chaudhury,Vishwanath Gaur,Brejesh Lall,Anupama Mallik +4 more
- Vol. 1, pp 438-445
Reads0
Chats0
TLDR
An unsupervised trend discovery approach that detects and correlates event patterns from videos temporally as well as spatially and utilizes geographic ontology (Geoontology) for identifying the various spatial patterns that exist corresponding to an event in a document.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.read more
Citations
More filters
Proceedings Article
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
TL;DR: "Data mining and knowledge discovery is over ten years old, and is now mature enough to begin to make some big plans and to tackle some very difficult problems and challenges" is the theme for this year's KDD Conference.
Journal ArticleDOI
Event-based media processing and analysis
Christos Tzelepis,Zhigang Ma,Vasileios Mezaris,Bogdan Ionescu,Ioannis Kompatsiaris,Giulia Boato,Nicu Sebe,Shuicheng Yan +7 more
TL;DR: This paper extensively review the employed conceptualization of the notion of event in multimedia, the techniques for event representation and modeling, the feature representation and event inference approaches for the problems of event detection in audio, visual, and textual content, and some key event-based multimedia applications and various benchmarking activities.
Journal ArticleDOI
A survey on multi-modal social event detection
TL;DR: This paper concentrates on conducting a comprehensive survey of extant works in multi-modal social event detection, reviewing two current attempts: event feature learning and event inference.
Book ChapterDOI
Topic and Thematic Description for Movies Documents
TL;DR: This work proposes a method that describe the theme and the topic of movies document based on the adaptation of the Latent Dirichlet Allocation (LDA) model by combining the textual and visual modalities from the pre-production movie document (Script) and from the superposed text in the image.
Survey on applications of ontology in various domains
TL;DR: This paper presents a survey of fifteen papers based on ontology, a body of knowledge describing some domain, typically common sense knowledge domain, used in web, mining and multi agent systems.
References
More filters
Journal ArticleDOI
Latent dirichlet allocation
TL;DR: This work proposes a generative model for text and other collections of discrete data that generalizes or improves on several previous models including naive Bayes/unigram, mixture of unigrams, and Hofmann's aspect model.
Proceedings Article
Latent Dirichlet Allocation
TL;DR: This paper proposed a generative model for text and other collections of discrete data that generalizes or improves on several previous models including naive Bayes/unigram, mixture of unigrams, and Hof-mann's aspect model, also known as probabilistic latent semantic indexing (pLSI).
Proceedings ArticleDOI
Discovering evolutionary theme patterns from text: an exploration of temporal text mining
Qiaozhu Mei,ChengXiang Zhai +1 more
TL;DR: This paper studies a particular TTM task -- discovering and summarizing the evolutionary patterns of themes in a text stream and presents general probabilistic methods for solving this problem through discovering latent themes from text and constructing an evolution graph of themes.
Text Mining: The state of the art and the challenges
TL;DR: A text mining framework consisting of two components: Text refining that transforms unstructured text documents into an intermediate form; and knowledge distillation that deduces patterns or knowledge from the intermediate form is presented.
Proceedings Article
Discovering trends in text databases
TL;DR: This work addresses the problem of discovering trends in text databases by defining a trend, a specific subsequence of the history of a phrase that satisfies the users’ query over the histories.