Quantum-inspired hashing for multimedia identification = 양자 개념을 도입한 멀티미디어 식별 기법

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In this thesis, a novel multimedia identification system based on quantum-inspired hashing is considered. Many traditional systems are based on binary hash which is obtained by encoding intermediate hash extracted from multimedia content. In the system considered, the intermediate hash values extracted from a query are encoded into quantum hash values by incorporating uncertainty in the binary hash values. For this, the intermediate hash difference between the query and its true-underlying content is considered as a random process. Then, the uncertainty is represented by the probability density estimate of the intermediate hash difference. The quantum-inspired hashing system is evaluated using both audio and video databases, and with marginal increment in computational cost, the quantum-inspired hashing system is shown to be more robust against various distortions than the binary hashing system using the same intermediate hash values. In addition, this thesis also considers a query video clip with shot insertion and deletion. Previous multimedia hashing systems have focused on extracting robust multimedia hash values against various quality degradations of video such as brightness change, frame rate change, camcoder attack, etc. Recently, some systems have focused on the temporal distortions such as linear play-speed changes. To consider the shot insertion and deletion, the query is modeled a cyclic Markov random field model whose nodes are associated with the shots in the query video. The latent variable of a node is defined as the index of binary hash vector in the database that matches with a shot. When the query is fed into the system, the system segments the query into shots to construct its graph. Then, the best-matched latent variables are estimated using the loopy belief propagation algorithm. To reduce the computational cost of the loopy belief propagation algorithm, the domain of the latent variable of a node is defined as the nearest neighbors of the...
Advisors
Yoo, Chang-Dongresearcher유창동researcher
Description
한국과학기술원 : 전기및전자공학전공,
Publisher
한국과학기술원
Issue Date
2009
Identifier
327788/325007  / 020045265
Language
eng
Description

학위논문(박사) - 한국과학기술원 : 전기및전자공학전공, 2009. 8., [ vii, 59 p. ]

Keywords

Multimedia Identification; 멀티미디어 식별; Multimedia Identification; 멀티미디어 식별

URI
http://hdl.handle.net/10203/35551
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=327788&flag=dissertation
Appears in Collection
EE-Theses_Ph.D.(박사논문)
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