Duplicate video detection for large-scale multimedia

Jun, Woogyoung; Lee, Yillbyung; Jun, Byoung-Min

doi:10.1007/s11042-015-2724-0

Cited by 8 publications

(2 citation statements)

References 23 publications

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“…Ching-Nung Yang et al [18] show a new data hiding scheme for verifying the embedding rate during the embedding and extracting phases. The proposed research BHamming+3,^which is a reasonably acceptable steganography method, shows better performance than BHamming+1.^Woogyoung Jun et al [13] proposed the duplicate video detection for large-scale multimedia that is based on block histogram. It uses a dynamic matching algorithm for fast and real-time process of large-scale data.…”

Section: Related Workmentioning

confidence: 99%

Guest Editorial: Large-scale Multimedia Data Management: Techniques and Applications

Hung

Takizawa

Chen

2016

Multimed Tools Appl

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Essential to many tasks in relation to multimedia research and development is the availability of a sufficiently large data set and its corresponding ground truth. However, most available data for multimedia research are either too specific, e.g., data for text retrieval; too small, e.g., face figures; nor without ground truth, such as gathering millions of un-preprocessing images from the Web for testing. While it is relatively easy to crawl and store a huge amount of data, the creation of ground truth necessary to systematically train, test, evaluate, and compare the performance of various algorithms and systems remains a challenging issue. For this reason, researchers tend to put (or redirect) efforts into the creation of such corpus individually to carry out research on large-scale data sets. Thus, a promising trend of a united web-scale and distributed multimedia data management is urgently needed, which would benefit the entire multimedia research community. This special issue presents and reports on the construction and analysis of large-scale multimedia data sets and resources, and provides a strong reference for multimedia researchers interested in large-scale multimedia data sets. In particular, the special issue demonstrates the emerging techniques and applications for large-scale multimedia data management.

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Section: Related Workmentioning

confidence: 99%

Guest Editorial: Large-scale Multimedia Data Management: Techniques and Applications

Hung

Takizawa

Chen

2016

Multimed Tools Appl

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“…These methods are still not robust enough to be applied to a large-scale database. Although a few algorithms [30][31][32] have been developed to retrieve a large-scale video database, their low efficiency limits their potential use on a web-scale dataset.…”

Section: Introductionmentioning

confidence: 99%

Large-scale video copy retrieval with temporal-concentration SIFT

Zhu

Huang²,

Huang

et al. 2016

Neurocomputing

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The scale-invariant feature transform (SIFT) feature plays a very important role in multimedia content analysis, such as nearduplicate image and video retrieval. However, the storage and query costs of SIFT become unbearable for large-scale databases. In this paper, SIFT features are robustly encoded with temporal information by tracking the SIFT to generate temporalconcentration SIFT (TCSIFT), which highly compresses the quantity of local features to reduce visual redundancy, and keeps the advantages of SIFT as much as possible at the same time. On the basis of TCSIFT, a novel framework for large-scale video copy retrieval is proposed in which the processes of retrieval and validation are implemented at the feature and frame level. Experimental results for two different datasets, i.e., CC_WEB_VIDEO and TRECVID, demonstrate that our method can yield comparable accuracy, compact storage size, and more efficient execution time, as well as adapt to various video transformations.

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