Abstract/Details

Techniques for scalability in multimedia retrieval


2007 2007

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Abstract (summary)

With the widespread adoption of devices and tools for the creation and capture of various media, there is a need to organize, and allow retrieval of, multimedia objects. While numerous techniques for content analysis and similarity measurement of multimedia objects are being developed, these techniques are not directly suitable for efficient retrieval from large multimedia databases. This thesis addresses four challenges faced for making multimedia retrieval scalable. First, a method is proposed to embed objects into a Euclidean space based on dissimilarities computed using an arbitrary, possibly expensive, similarity measure. The method creates an embedding with low computational cost, and the embedding can be used to efficiently compute approximate distances between objects. Second, a distance measure and indexing method is proposed to allow efficient retrieval based on partial similarities between objects. Unlike other measures that utilize partial similarity, this measure is metric, and allows a much lower retrieval cost. Third, we present a system for scalably finding landmarks in images using a local feature representation. Efficiency is achieved by embedding object representations into a Euclidean space. Fourth, an indexing method is proposed for answering Support Vector Machine (SVM) queries, which represent a concept learnt using a training set, and represented by an SVM classifier. This index is independent of the parameter used to train the SVM. Overall, this thesis makes a strong contribution to bridging the gap between multimedia content analysis and a scalable multimedia retrieval system.

Indexing (details)


Subject
Computer science
Classification
0984: Computer science
Identifier / keyword
Applied sciences; Embedding; Indexing; Multimedia; Scalability; Support vector machine
Title
Techniques for scalability in multimedia retrieval
Author
Qamra, Arun
Number of pages
194
Publication year
2007
Degree date
2007
School code
0035
Source
DAI-B 68/10, Dissertation Abstracts International
Place of publication
Ann Arbor
Country of publication
United States
ISBN
9780549269960
Advisor
Chang, Edward Y.
Committee member
Singh, Ambuj; Suri, Subhash
University/institution
University of California, Santa Barbara
Department
Computer Science
University location
United States -- California
Degree
Ph.D.
Source type
Dissertations & Theses
Language
English
Document type
Dissertation/Thesis
Dissertation/thesis number
3283766
ProQuest document ID
304882377
Copyright
Database copyright ProQuest LLC; ProQuest does not claim copyright in the individual underlying works.
Document URL
http://search.proquest.com/docview/304882377
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