Title page for etd-0723108-122600


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URN etd-0723108-122600
Author Yen-Ren Huang
Author's Email Address No Public.
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Department Computer Science and Engineering
Year 2007
Semester 2
Degree Master
Type of Document
Language zh-TW.Big5 Chinese
Title Retrieval by spatial similarity based on interval neighbor group
Date of Defense 2008-07-10
Page Count 99
Keyword
  • Multiple-instance learning
  • Content-based image retrieval
  • Retrieval by spatial similarity
  • Abstract The objective of the present work is to employ a multiple-instance learning image retrieval system by incorporating a spatial similarity measure. Multiple-Instance learning is a way of modeling ambiguity in supervised learning given multiple examples. From a small collection of positive and negative example images, semantically relevant concepts can be derived automatically and employed to retrieve images from an image database. The degree of similarity between two spatial relations is linked to the distance between the associated nodes in an Interval Neighbor Group (ING). The shorter the distance, the higher degree of similarity, while a longer one, a lower degree of similarity. Once all the pairwise similarity values are derived, an ensemble similarity measure will then integrate these pairwise similarity assessments and give an overall similarity value between two images. Therefore, images in a database can be quantitatively ranked according to the degree of ensemble similarity with the query image. Similarity retrieval method evaluates the ensemble similarity based on the spatial relations and common objects present in the maximum common subimage between the query and a database image are considered. Therefore, reliable spatial relation features extracted from the image, combined with a multiple-instance learning paradigm to derive relevant concepts, can produce desirable retrieval results that better match user’s expectation.
    In order to demonstrate the feasibility of the proposed approach, two sets of test for querying an image database are performed, namely, the proposed RSS-ING scheme v.s. 2D Be-string similarity method, and single-instance vs. multiple-instance learning. The performance in terms of similarity curves, execution time and memory space requirement show favorably for the proposed multiple-instance spatial similarity-based approach.
    Advisory Committee
  • Chung-Nan Lee - chair
  • Inn-Chyn Her - co-chair
  • Jung-Jae Chao - co-chair
  • Shuenn-Ren Cheng - co-chair
  • John Y. Chiang - advisor
  • Files
  • etd-0723108-122600.pdf
  • indicate in-campus access immediately and off_campus access in a year
    Date of Submission 2008-07-23

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