TIIS (한국인터넷정보학회)
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| 한글제목(Korean Title) |
Stochastic Non-linear Hashing for Near-Duplicate Video Retrieval using Deep Feature applicable to Large-scale Datasets |
| 영문제목(English Title) |
Stochastic Non-linear Hashing for Near-Duplicate Video Retrieval using Deep Feature applicable to Large-scale Datasets |
| 저자(Author) |
Sung-Woo Byun
Seok-Pil Lee
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| 원문수록처(Citation) |
VOL 13 NO. 08 PP. 4300 ~ 4314 (2019. 08) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
With the development of video-related applications, media content has increased dramatically through applications. There is a substantial amount of near-duplicate videos (NDVs) among Internet videos, thus NDVR is important for eliminating near-duplicates from web video searches. This paper proposes a novel NDVR system that supports large-scale retrieval and contributes to the efficient and accurate retrieval performance. For this, we extracted keyframes from each video at regular intervals and then extracted both commonly used features (LBP and HSV) and new image features from each keyframe. A recent study introduced a new image feature that can provide more robust information than existing features even if there are geometric changes to and complex editing of images. We convert a vector set that consists of the extracted features to binary code through a set of hash functions so that the similarity comparison can be more efficient as similar videos are more likely to map into the same buckets. Lastly, we calculate similarity to search for NDVs; we examine the effectiveness of the NDVR system and compare this against previous NDVR systems using the public video collections CC_WEB_VIDEO. The proposed NDVR system’s performance is very promising compared to previous NDVR systems.
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| 키워드(Keyword) |
Near-duplicate Video
NDVR
Class Activation Maps
Hashing
supervised learning
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| 파일첨부 |
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