Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

6-2017

Abstract

A problem not well understood in video hyperlinking is what qualifies a fragment as an anchor or target. Ideally, anchors provide good starting points for navigation, and targets supplement anchors with additional details while not distracting users with irrelevant, false and redundant information. The problem is not trivial for intertwining relationship between data characteristics and user expectation. Imagine that in a large dataset, there are clusters of fragments spreading over the feature space. The nature of each cluster can be described by its size (implying popularity) and structure (implying complexity). A principle way of hyperlinking can be carried out by picking centers of clusters as anchors and from there reach out to targets within or outside of clusters with consideration of neighborhood complexity. The question is which fragments should be selected either as anchors or targets, in one way to reflect the rich content of a dataset, and meanwhile to minimize the risk of frustrating user experience. This paper provides some insights to this question from the perspective of hubness and local intrinsic dimensionality, which are two statistical properties in assessing the popularity and complexity of data space. Based these properties, two novel algorithms are proposed for low-risk automatic selection of anchors and targets.

Keywords

Hubness, Local intrinsic dimensions, Video hyperlinking

Discipline

Computer Sciences | Graphics and Human Computer Interfaces

Research Areas

Intelligent Systems and Optimization

Publication

Proceedings of the 17th ACM International Conference on Multimedia Retrieval, ICMR 2017

First Page

287

Last Page

293

ISBN

9781450347013

Identifier

10.1145/3078971.3079025

Publisher

Association for Computing Machinery, Inc

City or Country

Bucharest

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