Hiranmay Ghosh

Multimedia explorer: Content based multimedia exploration

In this paper, we present an innovative way for effective interaction of users with the multimedia contents. We propose a novel framework which enables creation of content information through structural, semantic and media feature based descriptors compliant to MPEG-7 standard. The architecture offers content based search and personalized presentation using SMIL. The content based search exploits the MPEG-7 compliant content description to support spatio-temporal query constructs.

Smart Water Management: An Ontology-Driven Context-Aware IoT Application

This paper presents a context-aware ontology driven approach to water resource management in smart cities for providing adequate water supply to the citizens. The appropriate management of water requires exploitation of efficient action plan to review the prevailing causes of water shortage in a geospatial environment. This involves analysis of historical and real-time water specific information captured through heterogeneous sensors. Since the gathered contextual data is available in different formats so interoperability across diverse data requires converting it into a common perceivable RDF format. As the perceptual model of the Smart Water domain comprises of observable media properties of the concepts so to achieve context-aware data fusion we have employed multimedia ontology based semantic mapping. The multimedia ontology encoded in Multimedia Web Ontology Language (MOWL) forms the core of our IoT based smart water application. It supports Dynamic Bayesian Network based probabilistic reasoning to predict the changing situations in a real-time irregular environment patterns. Ultimately, the paper presents a context-aware approach to deal with uncertainties in water resource in the face of environment variability and offer timely conveyance to water authorities by circulating warnings via text-messages or emails. To illustrate the usability of the presented approach we have utilized the online available sample water data-sets.

An IoT approach for context-aware smart traffic management using ontology

This paper exhibits a novel context-aware service framework for IoT based Smart Traffic Management using ontology to regulate smooth traffic flow in smart cities by analyzing real-time traffic environment. The proposed approach makes smarter use of transport networks to achieve objectives related to performance of transport system. This requires efficient traffic planning measures which relate to the actions designed to adjust the demand and capacity of the network in time and space by use of IoT technologies. The adoption of sensors and IoT devices in Smart Traffic System helps to capture the users preferences and context information which can be in the form of travel time, weather conditions or real-life driving patterns. We have employed multimedia ontology to derive higher level descriptions of traffic conditions and vehicles from perceptual observation of traffic information which provides important grounds for our proposed IoT framework. The multimedia ontology encoded in Multimedia Web Ontology Language(MOWL) helps to define classes, properties, and structure of a possible traffic environment to provide insights across the transportation network. MOWL supports Dynamic Bayesian networks (DBN) to deal with time-series data and uncertainties linked with context observations which fits the definition of an intelligent IoT system. Thus, our proposed smart traffic framework aggregates information corresponding to traffic domain such as traffic videos captured using CCTV cameras and allows automatic prediction of dynamically changing situations which helps to make traffic authorities more responsive. We have illustrated use of our approach by utilizing contextual information, to assess real-time congestion situation on roads thus allowing to visualize planning services. Once the congestion situation is predicted, alternate congestion free routes which are in accordance with the coveted criteria are suggested that can be propagated through text-messages or e-mails to the users.

Efficient and Language Independent News Story Segmentation for Telecast News Videos

A TV news program comprises a continuous video stream containing a number of news stories, interspersed with commercials and headlines. This paper presents a method to detect the story boundaries and to separate out the stories from the other components and from each other. The method is based on movement of ticker text bands and repetition of ticker texts during different parts of a news program. The method does not use any language processing tool and is independent of language of telecast. It uses some simple features to distinguish news from the advertisements and can be used for large scale news indexing. We produce some test results on channels telecasting in English and few other Indian languages.

Data Marketplace as a Platform for Sharing Scientific Data

Data marketplace is an emerging service model to facilitate data exchange between its producers and consumers. While the service has been motivated by a business model for data and has established itself in the commercial sector over the last few years, it is possible to build a data sharing platform for the scientific community on this model. This article analyzes the motivational and technical challenges for scientific data exchange and proposes use of data marketplace service model to address them.

Indexing of multilingual news telecast using audio-visual keywords

Indexing of news video streams with semantic keywords is of interest to agencies that regularly monitor many news channels. In this paper, we describe a new method for indexing news video in different languages, for which there are inadequate language tools. Our approach involves combining multimodal inputs, namely audio and visual, and spotting of a handful of keywords with higher reliability, as compared to creating complete transcripts. We conduct a set of experiments to establish performance improvement despite lesser reliability of the language tools.