Near-duplicate video clustering using multiple complementary video signatures

Jun Tae Lee; Kyung Rae Kim; Won Dong Jang; Chang-Su Kim

doi:10.1109/APSIPA.2015.7415354

Near-duplicate video clustering using multiple complementary video signatures

Jun Tae Lee
, Kyung Rae Kim
, Won Dong Jang
, Chang-Su Kim

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

A near-duplicate video clustering algorithm based on multiple complementary video signatures is proposed in this work. We use three kinds of frame descriptors: RGB histogram, color name histogram, and ternary pattern. Then, we convert each kind of frame descriptors for a video into a video signature based on the bag-of-visual-words scheme. Consequently, we have three signatures to represent the video. These signatures are complementary to one another, since they are robust to different near-duplication types. Also, we develop a clustering technique to refine pairwise matching results and categorize near-duplicate videos. Experimental results on an extensive video dataset show that the proposed algorithm detects near-duplicate videos more effectively than conventional algorithms.

Original language	English
Title of host publication	2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2015
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	667-671
Number of pages	5
ISBN (Electronic)	9789881476807
DOIs	https://doi.org/10.1109/APSIPA.2015.7415354
Publication status	Published - 2016 Feb 19
Event	2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2015 - Hong Kong, Hong Kong Duration: 2015 Dec 16 → 2015 Dec 19

Other

Other	2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2015
Country/Territory	Hong Kong
City	Hong Kong
Period	15/12/16 → 15/12/19

ASJC Scopus subject areas

Artificial Intelligence
Modelling and Simulation
Signal Processing

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10.1109/APSIPA.2015.7415354

Cite this

Lee, J. T., Kim, K. R., Jang, W. D., & Kim, C.-S. (2016). Near-duplicate video clustering using multiple complementary video signatures. In 2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2015 (pp. 667-671). Article 7415354 Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/APSIPA.2015.7415354

Near-duplicate video clustering using multiple complementary video signatures. / Lee, Jun Tae; Kim, Kyung Rae; Jang, Won Dong et al.
2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2015. Institute of Electrical and Electronics Engineers Inc., 2016. p. 667-671 7415354.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Lee, JT, Kim, KR, Jang, WD & Kim, C-S 2016, Near-duplicate video clustering using multiple complementary video signatures. in 2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2015., 7415354, Institute of Electrical and Electronics Engineers Inc., pp. 667-671, 2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2015, Hong Kong, Hong Kong, 15/12/16. https://doi.org/10.1109/APSIPA.2015.7415354

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title = "Near-duplicate video clustering using multiple complementary video signatures",

abstract = "A near-duplicate video clustering algorithm based on multiple complementary video signatures is proposed in this work. We use three kinds of frame descriptors: RGB histogram, color name histogram, and ternary pattern. Then, we convert each kind of frame descriptors for a video into a video signature based on the bag-of-visual-words scheme. Consequently, we have three signatures to represent the video. These signatures are complementary to one another, since they are robust to different near-duplication types. Also, we develop a clustering technique to refine pairwise matching results and categorize near-duplicate videos. Experimental results on an extensive video dataset show that the proposed algorithm detects near-duplicate videos more effectively than conventional algorithms.",

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AB - A near-duplicate video clustering algorithm based on multiple complementary video signatures is proposed in this work. We use three kinds of frame descriptors: RGB histogram, color name histogram, and ternary pattern. Then, we convert each kind of frame descriptors for a video into a video signature based on the bag-of-visual-words scheme. Consequently, we have three signatures to represent the video. These signatures are complementary to one another, since they are robust to different near-duplication types. Also, we develop a clustering technique to refine pairwise matching results and categorize near-duplicate videos. Experimental results on an extensive video dataset show that the proposed algorithm detects near-duplicate videos more effectively than conventional algorithms.

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Near-duplicate video clustering using multiple complementary video signatures

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