Fast search method for large video database using histogram features and temporal division

Feifei Lee, Qiu Chen, Koji Kotani, Tadahiro Ohmi

Research output: Contribution to journalArticlepeer-review

Abstract

In this paper, we propose an improved fast search algorithm using combined histogram features and temporal division method for short MPEG video clips from large video database. There are two types of histogram features used to generate more robust features. The first one is based on the adjacent pixel intensity difference quantization (APIDQ) algorithm, which had been reliably applied to human face recognition previously. An APIDQ histogram is utilized as the feature vector of the frame image. Another one is ordinal feature which is robust to color distortion. Combined with active search [4], a temporal pruning algorithm, fast and robust video search can be realized. The proposed search algorithm has been evaluated by 6 hours of video to search for given 200 MPEG video clips which each length is 30 seconds. Experimental results show the proposed algorithm can detect the similar video clip in merely 120ms, and Equal Error Rate (ERR) of 1% is achieved, which is more accurately and robust than conventional fast video search algorithm.

Original languageEnglish
Pages (from-to)167-170
Number of pages4
JournalWorld Academy of Science, Engineering and Technology
Volume70
Publication statusPublished - 2010 Sep 1

Keywords

  • Adjacent pixel intensity difference quantization (APIDQ)
  • DC image
  • Fast search
  • Histogram feature

ASJC Scopus subject areas

  • Engineering(all)

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