Automatic detection of salient objects and spatial relations in videos for a video database system

Abstract

Multimedia databases have gained popularity due to rapidly growing quantities of multimedia data and the need to perform efficient indexing, retrieval and analysis of this data. One downside of multimedia databases is the necessity to process the data for feature extraction and labeling prior to storage and querying. Huge amount of data makes it impossible to complete this task manually. We propose a tool for the automatic detection and tracking of salient objects, and derivation of spatio-temporal relations between them in video. Our system aims to reduce the work for manual selection and labeling of objects significantly by detecting and tracking the salient objects, and hence, requiring to enter the label for each object only once within each shot instead of specifying the labels for each object in every frame they appear. This is also required as a first step in a fully-automatic video database management system in which the labeling should also be done automatically. The proposed framework covers a scalable architecture for video processing and stages of shot boundary detection, salient object detection and tracking, and knowledge-base construction for effective spatio-temporal object querying.

Analysis and Implementation of Methodologies for the Monitoring of Changes in Eye Fundus Images

“…Also, changes could be demarcated as areas filled through the use of bounding boxes [21][22]. The latter help to define areas of changes rather than changes, which would facilitate the specialist to evaluate in the same image the significant signs without obstruct her/his observation.…”

Semantic based representing and organizing surveillance big data using video structural description technology

“…Obviously, using spatial or temporal relations between objects in videos is important for achieving accurate extraction of events. Researches such as BilVideo (Donderler et al, 2005), extended-AVIS (Sevilmis et al, 2008), multiView (Fan et al, 2001) and classView (Fan et al, 2004) used spatial and temporal relations but do not have ontology-based models for semantic content representation. Bai et al (2007) presented a semantic based framework using domain ontology.…”

Fuzzy Ontology and Rule based Model for Automatic Semantic Content Extraction from Videos using k-Means Algorithm

“…BilVideo is the video database management system provides full support for queries on spatio-temporal, low level features and semantic on the video data [7] [8]. It is domain independent and to handle the spatio-temporal queries a knowledge-base is used that consists of a fact-base and comprehensive set of rules which reduce the number of facts.…”