Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

6-2016

Abstract

Complex video event detection without visual examples is a very challenging issue in multimedia retrieval. We present a state-of-the-art framework for event search without any need of exemplar videos and textual metadata in search corpus. To perform event search given only query words, the core of our framework is a large, pre-built bank of concept detectors which can understand the content of a video in the perspective of object, scene, action and activity concepts. Leveraging such knowledge can effectively narrow the semantic gap between textual query and the visual content of videos. Besides the large concept bank, this paper focuses on two challenges that largely affect the retrieval performance when the size of the concept bank increases: (1) How to choose the right concepts in the concept bank to accurately represent the query; (2) if noisy concepts are inevitably chosen, how to minimize their influence. We share our novel insights on these particular problems, which paves the way for a practical system that achieves the best performance in NIST TRECVID 2015.

Keywords

0ex; Concept bank, Concept selection, Multimedia event detection, Semantic pooling, Video search

Discipline

Graphics and Human Computer Interfaces | Theory and Algorithms

Research Areas

Intelligent Systems and Optimization

Publication

Proceedings of the 6th ACM International Conference on Multimedia Retrieval, ICMR 2016, New York, June 6-9

First Page

127

Last Page

134

ISBN

9781450343596

Identifier

10.1145/2911996.2912015

Publisher

ACM

City or Country

New York

Download

Share

COinS