Efficient Video Stream Monitoring for Near-Duplicate Detection and Localization in a Large-Scale Repository

  • Authors:

  • Chih-Yi Chiu;Tsung-Han Tsai;Guei-Wun Han;Cheng-Yu Hsieh;Sheng-Yang Li

  • Affiliations:

  • National Chiayi University;National Chiayi University;National Chiayi University;National Chiayi University;National Chiayi University

  • Venue:
  • ACM Transactions on Information Systems (TOIS)
  • Year:
  • 2013

Quantified Score

Hi-index 0.00

Visualization

Abstract

In this article, we study the efficiency problem of video stream near-duplicate monitoring in a large-scale repository. Existing stream monitoring methods are mainly designed for a short video to scan over a query stream; they have difficulty being scalable for a large number of long videos. We present a simple but effective algorithm called incremental similarity update to address the problem. That is, a similarity upper bound between two videos can be calculated incrementally by leveraging the prior knowledge of the previous calculation. The similarity upper bound takes a lightweight computation to filter out unnecessary time-consuming computation for the actual similarity between two videos, making the search process more efficient. We integrate the algorithm with inverted indexing to obtain a candidate list from the repository for the given query stream. Meanwhile, the algorithm is applied to scan each candidate for locating exact near-duplicate subsequences. We implement several state-of-the-art methods for comparison in terms of accuracy, execution time, and memory consumption. Experimental results demonstrate the proposed algorithm yields comparable accuracy, compact memory size, and more efficient execution time.