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Title page for etd-0823111-121408


URN etd-0823111-121408 Statistics This thesis had been viewed 1825 times. Download 525 times.
Author Mann-Jung Hsiao
Author's Email Address No Public.
Department Computer Science and Enginerring
Year 2010 Semester 2
Degree Ph.D. Type of Document Doctoral Dissertation
Language English Page Count 158
Title A General and Effective Two-Stage Approach for Region-Based Image Retrieval
Keyword
  • discrete cosine transform
  • region of interest
  • content-based image retrieval
  • region-based image retrieval
  • threshold-based pruning
  • threshold-based pruning
  • region-based image retrieval
  • content-based image retrieval
  • region of interest
  • discrete cosine transform
  • Abstract With the rapid growth of multimedia applications and digital archives, content-based image retrieval (CBIR) has received lots of attentions and emerged as an important research area for the past decades. CBIR tends to automatically index and retrieve images based on their low-level contents, which is a complex and challenging problem spanning diverse algorithms all over the retrieval processes including color space selection, feature extraction, similarity measurement, retrieval strategies, relevance feedback, etc. In these issues, “semantic gap” is still an open challenging problem in CBIR. It reflects the discrepancy between low-level features developed by the retrieval algorithm and high-level concepts required by users. Some research works attempt to narrow this gap by utilizing regional features, which are known as region-based image retrieval (RBIR).
    RBIR tends to search the interesting regions that are closed to the query target, instead of the whole images. It contributes to more meaningful image retrieval; however, the image segmentation algorithms are complex and computation intensive and the segmentation results are often not correct. To tackle this problem, we propose a two-stage retrieval strategy to improve the performance of RBIR. At the first stage of retrieval, the threshold-based pruning (TBP) serves as a filter to quickly remove those candidates with widely distinct global features. At the second stage, a more detailed feature comparison (DFC) method is conducted over the remaining candidates, focusing on the region of interest (ROI). In the experimental system, users can choose their ROI in the query image and interact with the system by selecting different strategies, setting parameter values, and adjusting the weights of features as the search progresses. The experimental results show that both efficiency and accuracy can be respectively improved by 10.7% and 7.1% using the proposed two-stage approach.
    Advisor Committee
  • Shang-lin Hsieh - advisor
  • Yo-Ping Huang - advisor
  • none - co-chair
  • none - co-chair
  • none - co-chair
  • none - co-chair
  • Files indicate in-campus access at 2 years and off-campus access at 2 years
    Date of Defense 2011-07-13 Date of Submission 2011-08-23


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