Natural language querying for video databases

Abstract

The video databases have become popular in various areas due to the recent advances in technology. Video archive systems need user-friendly interfaces to retrieve video frames. In this paper, a user interface based on natural language processing (NLP) to a video database system is described. The video database is based on a content-based spatio-temporal video data model. The data model is focused on the semantic content which includes objects, activities, and spatial properties of objects. Spatio-temporal relationships between video objects and also trajectories of moving objects can be queried with this data model. In this video database system, a natural language interface enables flexible querying. The queries, which are given as English sentences, are parsed using link parser. The semantic representations of the queries are extracted from their syntactic structures using information extraction techniques. The extracted semantic representations are used to call the related parts of the underlying video database system to return the results of the queries. Not only exact matches but similar objects and activities are also returned from the database with the help of the conceptual ontology module. This module is implemented using a distance-based method of semantic similarity search on the semantic domain-independent ontology, WordNet.

ONLI: An ontology-based system for querying DBpedia using natural language paradigm

“…The above alternatives measures of interrater agreement are commonly applied in information retrieval experiments (Hripcsak & Rothschild, 2005). Also, these metrics are widely used by researchers in the context of NLI development (Cimiano et al, 2008;Erozel, Cicekli, & Cicekli, 2008;Minock, 2010).…”

CoTO: A Novel Approach for Fuzzy Aggregation of Semantic Similarity Measures

“…The reason is that these techniques can be used for going beyond the literal lexical match of words and text expressions. Past works in this field include the automatic processing of text and email messages [18], healthcare dialogue systems [5], natural language querying of databases [14], question answering [25], and sentence fusion [2].…”

Real-time selection of video streams for live TV broadcasting based on Query-by-Example using a 3D model

“…Finally, it is interesting to mention that other proposals focus on the problem of retrieving 3D object models (e.g, [31,32,33,34,35]) or videos (e.g., [36]) instead of images. To improve the efficiency and accuracy of view-based 3D object retrieval, in [35] the authors propose to select the most interesting 2D views using a probabilistic Bayesian method (Adaptive Views Clustering), whereas in [33] the authors present an algorithm that minimizes the number of query views required based on information extracted from the query and the users' relevance feedback.…”