Abstract/Details

Statistical models for text query-based image retrieval


2008 2008

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Abstract (summary)

Image indexing and retrieval has been an active research area for more than one decade. Although many accomplishments have been made in this domain, it is still a challenging problem and far from being solved. Traditional content-based approaches make use of queries based on image examples or image attributes like color and texture, and images are retrieved according to the similarity of each target image with the query image. However, image query based retrieval systems do not really capture the semantics or meanings of images well. Furthermore, image queries are difficult and inconvenient to form for most users.

To capture the semantics of images, libraries and other organizations have manually annotated each image with keywords and captions, and then search on those annotations using text retrieval engines. The disadvantage of this approach is the huge cost of annotating large number of images and the inconsistency of annotations by different people. In this work, we focus on general image and historical handwritten document retrieval based on textual queries. We explore statistical model based techniques that allow us to retrieve general images and historical handwritten document images with text queries. These techniques are (i) image retrieval based on automatic annotation, (ii) direct retrieval based on computing the posterior of an image given a text query, and (iii) handwritten document image recognition. We compare the performance of these approaches on several general image and historical handwritten document collections. The main contributions of this work include (i) two probabilistic generative models for annotation-based retrieval, (ii) a direct retrieval model for general images, and (iii) a thorough investigation of machine learning models for handwritten document recognition. Our experimental results and retrieval systems show that our proposed approaches may be applied to practical textual query based retrieval systems on large image data sets.

Indexing (details)


Subject
Computer science
Classification
0984: Computer science
Identifier / keyword
Applied sciences; Image annotation; Image retrieval; Information retrieval; Statistical models; Text-based queries; Video retrieval
Title
Statistical models for text query-based image retrieval
Author
Feng, Shaolei
Number of pages
159
Publication year
2008
Degree date
2008
School code
0118
Source
DAI-B 69/09, Dissertation Abstracts International
Place of publication
Ann Arbor
Country of publication
United States
ISBN
9780549789383
Advisor
Manmatha, R.
Committee member
Allan, James; Hanson, Allen R.; Kelly, Patrick A.; Manmatha, R.
University/institution
University of Massachusetts Amherst
Department
Computer Science
University location
United States -- Massachusetts
Degree
Ph.D.
Source type
Dissertations & Theses
Language
English
Document type
Dissertation/Thesis
Dissertation/thesis number
3325262
ProQuest document ID
304567004
Copyright
Database copyright ProQuest LLC; ProQuest does not claim copyright in the individual underlying works.
Document URL
http://search.proquest.com/docview/304567004
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