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RISS 인기검색어
- 검색키워드
- 주제어 : probabilistic dissimilarity (검색결과 3 건)
- 무료
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A Probabilistic Dissimilarity Matching for the DFT-Domain Image Hashing
서진수,조명석 국제문화기술진흥원 2017 International Journal of Advanced Culture Technolo Vol.5 No.1
An image hash, a discriminative and robust summary of an image, should be robust against quality-preserving signal processing steps, while being pairwise independent for perceptually different inputs. In order to improve the hash matching performance, this paper proposes a probabilistic dissimilarity matching. Instead of extracting the binary hash from the query image, we compute the probability that the intermediate hash vector of the query image belongs to each quantization bin, which is referred to as soft quantization binning. The probability is used as a weight in comparing the binary hash of the query with that stored in a database. A performance evaluation over sets of image distortions shows that the proposed probabilistic matching method effectively improves the hash matching performance as compared with the conventional Hamming distance.
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A Probabilistic Dissimilarity Matching for the DFT-Domain Image Hashing
Seo, Jin S.,Jo, Myung-Suk The International Promotion Agency of Culture Tech 2017 International Journal of Advanced Culture Technolo Vol.5 No.1
An image hash, a discriminative and robust summary of an image, should be robust against quality-preserving signal processing steps, while being pairwise independent for perceptually different inputs. In order to improve the hash matching performance, this paper proposes a probabilistic dissimilarity matching. Instead of extracting the binary hash from the query image, we compute the probability that the intermediate hash vector of the query image belongs to each quantization bin, which is referred to as soft quantization binning. The probability is used as a weight in comparing the binary hash of the query with that stored in a database. A performance evaluation over sets of image distortions shows that the proposed probabilistic matching method effectively improves the hash matching performance as compared with the conventional Hamming distance.
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A Probabilistic Dissimilarity Matching for the DFT-Domain Image Hashing
Jin S. Seo,Myung-Suk Jo 국제문화기술진흥원 2017 International Journal of Advanced Culture Technolo Vol.5 No.1
An image hash, a discriminative and robust summary of an image, should be robust against quality-preserving signal processing steps, while being pairwise independent for perceptually different inputs. In order to improve the hash matching performance, this paper proposes a probabilistic dissimilarity matching. Instead of extracting the binary hash from the query image, we compute the probability that the intermediate hash vector of the query image belongs to each quantization bin, which is referred to as soft quantization binning. The probability is used as a weight in comparing the binary hash of the query with that stored in a database. A performance evaluation over sets of image distortions shows that the proposed probabilistic matching method effectively improves the hash matching performance as compared with the conventional Hamming distance.
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