Negative-Based Sampling for Multimedia Retrieval

Hsin Yu Ha, Shu Ching Chen, Mei Ling Shyu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

Nowadays, in such a high-tech living lifestyle, profusion of multimedia data are produced and propagated around the world. To identify meaningful semantic concepts from the large amount of data, one of the major challenges is called the data imbalance problem. Data imbalance occurs when the number of positive instances (i.e., instances which contain the target concept) is greatly less than the number of negative instances (i.e., instances which do not contain the target concept). In other words, the ratio between positive and negative instances is extremely low. Rebalancing the dataset is usually proposed to resolve the problem by sampling or data pruning. In this paper, we propose a sampling method which consists of three stages, namely selecting features to identify the negative instances, producing negative ranking scores, and performing sampling. The method is compared with some other existing methods on the TRECVID dataset and is demonstrated to have better performance.

Original languageEnglish (US)
Title of host publicationProceedings - 2015 IEEE 16th International Conference on Information Reuse and Integration, IRI 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages64-71
Number of pages8
ISBN (Electronic)9781467366564
DOIs
StatePublished - Oct 19 2015
Event16th IEEE International Conference on Information Reuse and Integration, IRI 2015 - San Francisco, United States
Duration: Aug 13 2015Aug 15 2015

Publication series

NameProceedings - 2015 IEEE 16th International Conference on Information Reuse and Integration, IRI 2015

Other

Other16th IEEE International Conference on Information Reuse and Integration, IRI 2015
CountryUnited States
CitySan Francisco
Period8/13/158/15/15

Keywords

  • FC-MST
  • Feature Selection
  • Multimedia
  • Sampling

ASJC Scopus subject areas

  • Information Systems
  • Information Systems and Management
  • Electrical and Electronic Engineering

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