G
Gaurav Harit
Researcher at Indian Institute of Technology, Jodhpur
Publications - 74
Citations - 630
Gaurav Harit is an academic researcher from Indian Institute of Technology, Jodhpur. The author has contributed to research in topics: Image segmentation & Character (mathematics). The author has an hindex of 13, co-authored 73 publications receiving 523 citations. Previous affiliations of Gaurav Harit include Indian Institutes of Technology & Indian Institute of Technology Delhi.
Papers
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Proceedings ArticleDOI
Using Multimedia Ontology for Generating Conceptual Annotations and Hyperlinks in Video Collections
TL;DR: This work presents a novel approach for defining video domain concepts in an ontology using properties that can be observed from the media and proposes the use of Bayesian network as the reasoning mechanism for doing inferencing tasks in the presence of uncertainty.
Proceedings ArticleDOI
Ontology guided access to document images
TL;DR: This paper makes use of an extension of OWL (ontology language for Web) to allow encoding of ontologies for document images to support conceptual querying and automated hyperlinking of document images.
Journal ArticleDOI
Topographic feature extraction for bengali and hindi character images
Soumen Bag,Gaurav Harit +1 more
TL;DR: Novel features based on the topography of a character as visible from different viewing directions on a 2D plane as represented as a shapebased graph which acts as an invariant feature set for discriminating very similar type characters efficiently.
Proceedings ArticleDOI
Document Retrieval with Unlimited Vocabulary
TL;DR: This paper uses SVM classifiers for word retrieval, and argues that the classifier based solutions can be superior to the OCR based solutions in many practical situations, and designs a one-shot learning scheme for dynamically synthesizing classifiers.
Proceedings Article
Enhanced Video Representation Using Objects.
TL;DR: This paper presents a new objectbased video representation paradigm, using appearance spaces, that enables fully automated extraction of semantic video objects for a class of sequences, and the development of a compressed, highly flexible representation of the video sequence based on extracted content.