Personalized Video Similarity Measure

Publication Type

Journal Article

Publication Date

2011

Abstract

As an effective technique to manage and explore large scale of video collections, personalized video search has received great attentions in recent years. One of the key problems in the related technique development is how to design and evaluate the similarity measures. Most of the existing approaches simply adopt traditional Euclidean distance or its variants. Consequently, they generally suffer from two main disadvantages: (1) low effectiveness—retrieval accuracy is poor. One of main reasons is that very little research has been carried out on designing an effective fusion scheme for integrating multimodal information (e.g., text, audio and visual) from video sequences and (2) poor scalability—development process of the video similarity metrics is largely disconnected from that of the relevant database access methods (indexing structures). This article reports a new distance metric called personalized video distance to effectively fuse information about individual preference and multimodal properties into a compact signature. Moreover, a novel hashing-based indexing structure has been designed to facilitate fast retrieval process and better scalability. A set of comprehensive empirical studies have been carried out based on two large video test collections and carefully designed queries with different complexities. We observe significant improvements over the existing techniques on various aspects.

Keywords

Video search, Similarity measure, Indexing structure, Scalability

Discipline

Databases and Information Systems | Graphics and Human Computer Interfaces

Publication

Multimedia Systems

Volume

17

Issue

5

First Page

421

Last Page

433

ISSN

0942-4962

Identifier

10.1007/s00530-010-0223-8

Publisher

Springer Verlag

Additional URL

http://dx.doi.org/10.1007/s00530-010-0223-8

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