Title

Efficient and cost-effective techniques for browsing and indexing large video databases

Authors

Authors

J. Oh;K. A. Hua

Comments

Authors: contact us about adding a copy of your work at STARS@ucf.edu

Abbreviated Journal Title

Sigmod Rec.

Keywords

shot detection; video indexing; video browsing; video similarity model; video retrieval; Computer Science, Information Systems; Computer Science, Software; Engineering

Abstract

We present in this paper a fully automatic content-based approach to organizing and indexing video data. Our methodology involves three steps: Step 1: We segment each video into shots using a Camera-Tracking technique. This process also extracts the feature vector for each shot, which consists of two statistical variances Var(BA) and Var(OA). These values capture how much things are changing in the background and foreground areas of the video shot. Step 2: For each video, We apply a fully automatic method to build a browsing hierarchy using the shots identified in Step 1. Step 3: Using the Var(BA) and Var(OA) values obtained in Step 1, we build an index table to support a variance-based video similarity model. That is, video scenes/shots are retrieved based on given values of Var(BA) and Var(OA) The above three inter-related techniques offer an integrated framework for modeling, browsing, and searching large video databases. Our experimental results indicate that they have many advantages over existing methods.

Journal Title

Sigmod Record

Volume

29

Issue/Number

2

Publication Date

1-1-2000

Document Type

Article; Proceedings Paper

Language

English

First Page

415

Last Page

426

WOS Identifier

WOS:000087867500037

ISSN

0163-5808

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