Visual duplicate based topic linking using a robust video signature

Iwamoto, Kota; Sato, Takami; Oami, Ryoma; Nomura, Toshiyuki

doi:10.1109/icce.2013.6486895

Cited by 3 publications

(4 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The use of intensity differences between various sub-regions in a frame is presented in [26]. The intensity differences are quantized into ternary vectors that are encoded to form a compact representation.…”

Section: Overview Of Previous Work In Video Signaturesmentioning

confidence: 99%

An HEVC compressed domain content-based video signature for copy detection and video retrieval

et al. 2014

View full text Add to dashboard Cite

Video sharing platforms and social networks have been growing very rapidly for the past few years. The rapid increase in the amount of video content introduces many challenges in terms of copyright violation detection and video search and retrieval. Generating and matching content-based video signatures, or fingerprints, is an effective method to detect copies or "near-duplicate" videos. Video signatures should be robust to changes in the video features used to characterize the signature caused by common signal processing operations. Recent work has focused on generating video signatures based on the uncompressed domain. However, decompression is a computationally intensive operation. In large video databases, it becomes advantageous to create robust signatures directly from the compressed domain. The High Efficiency Video Coding (HEVC) standard has been recently ratified as the latest video coding standard and wide spread adoption is anticipated. We propose a method in which a content-based video signature is generated directly from the HEVC-coded bitstream. Motion vectors from the HEVC-coded bitstream are used as the features. A robust hashing function based on projection on random matrices is used to generate the hashing bits. A sequence of these bits serves as the signature for the video. Our experimental results show that our proposed method generates a signature robust to common signal processing techniques such as resolution scaling, brightness scaling and compression.

show abstract

Section: Overview Of Previous Work In Video Signaturesmentioning

confidence: 99%

An HEVC compressed domain content-based video signature for copy detection and video retrieval

et al. 2014

View full text Add to dashboard Cite

show abstract

“…The purpose of almost all of the techniques is to detect similar contents at the ID level and positions are only roughly-estimated for enhancing system response and performance. Moreover the video signature technique [5] is adopted in MPEG7 for detecting video duplicates. They usually have not focused on the detection of the boundary between shots in the query.…”

Section: B Video Retrievalmentioning

confidence: 99%

“…In response, several kinds of video shot detection methods and video content detection methods have been proposed and developed, e.g. [1,2,3,4,5]. Video shot detection methods [1,2] usually detect shots, sequences of similar pictures taken by one camera, by analyzing the relation among neighbor frames.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Video Content Detection with Single Frame Level Accuracy Using Dynamic Thresholding Technique

Mori

Kitasaka

Nagano

et al. 2014

2014 22nd International Conference on Pattern Recognition

View full text Add to dashboard Cite

This paper proposes a video retrieval method that detects frame sections that correspond to shots in a query (video segment) with single frame level accuracy. The method adopts the coarse-to-fine strategy to decrease the processing time and the memory consumption; dynamic threshold with initial ranges for small segments is proposed to detect the exact beginning and end of each corresponding frame section to each shot in a query. Experiments on real videos show that our method can achieve accurate video detection with exact frame position while reducing processing time and memory consumption.

show abstract