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Hierarchical Visual Content Modelling and Query based on Trees

Setyanto, Arief (2016) Hierarchical Visual Content Modelling and Query based on Trees. PhD thesis, University of Essex.

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Abstract

In recent years, such vast archives of video information have become available that human annotation of content is no longer feasible; automation of video content analysis is therefore highly desirable. The recognition of semantic content in images is a problem that relies on prior knowledge and learnt information and that, to date, has only been partially solved. Salient analysis, on the other hand, is statistically based and highlights regions that are distinct from their surroundings, while also being scalable and repeatable. The arrangement of salient information into hierarchical tree structures in the spatial and temporal domains forms an important step to bridge the semantic salient gap. Salient regions are identified using region analysis, rank ordered and documented in a tree for further analysis. A structure of this kind contains all the information in the original video and forms an intermediary between video processing and video understanding, transforming video analysis to a syntactic database analysis problem. This contribution demonstrates the formulation of spatio-temporal salient trees the syntax to index them, and provides an interface for higher level cognition in machine vision.

Item Type: Thesis (PhD)
Uncontrolled Keywords: Video Segmentation, Video Analysis, Object Video Segmentation, Salient, Binary Partition Tree
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4050 Electronic information resources
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Arief Setyanto
Date Deposited: 15 Jun 2016 09:20
Last Modified: 15 Jun 2016 09:20
URI: http://repository.essex.ac.uk/id/eprint/16903

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