首頁 > 網路資源 > 大同大學數位論文系統

Title page for etd-0731107-150857


URN etd-0731107-150857 Statistics This thesis had been viewed 1850 times. Download 9 times.
Author Tsun-Wei Chang
Author's Email Address No Public.
Department Computer Science and Enginerring
Year 2006 Semester 2
Degree Ph.D. Type of Document Doctoral Dissertation
Language English Page Count 119
Title A GA-based Recommender Strategy for Image Retrieval
Keyword
  • image data mining
  • genetic algorithm
  • relevance feedback
  • region of interest
  • image segmentation
  • Image retrieval
  • Image retrieval
  • image segmentation
  • region of interest
  • relevance feedback
  • genetic algorithm
  • image data mining
  • Abstract Along with the advanced information technologies, the availability of World Wide Web together with the rapid growth of photographic archives have attracted our research motivation in providing an efficient access to the digital image database through browsing and searching. Content-based image retrieval (CBIR) has been intensively studied in the last decades. In this dissertation, some existing CBIR systems and related literatures are reviewed, and the focusing issues of retrieval strategies are addressed. We emphasize the topics of effective image segmentation, fast image retrieval model and efficient relevance feedback mechanism.
    This dissertation proposes an efficient genetic algorithm-based image retrieval strategy that applies the regions of interest and relevance feedback mechanism to improve the retrieval efficiency. Three main contributions have been achieved. (1) A fuzzy inference model is presented to derive an effective image segmentation method. A set of higher order statistical descriptors are used to represent the characteristics of a region content. (2) A GA-based image retrieval model is proposed. To assist the users to formulate more precise queries, the proposed system allows users to choose specific regions from multiple images. According to the human preference, the combination of image content descriptors from the selected regions forms the chromosomes of the genetic algorithm used for retrieving the target images. (3) The user relevance feedback mechanism is employed to direct the advanced search. The selected regions by a user are transformed into a transaction record. Furthermore, the retrieval performance is further improved by mining association rules from the retrieval feedback.
    The system architecture and methodology are detailed in this dissertation and thorough experiments on different queries demonstrate the effectiveness and scalability of the proposed strategy. Meanwhile, the prospects of future research directions and topics are also given in the conclusion chapter.
    Advisor Committee
  • Yo-Ping Huang - advisor
  • none - co-chair
  • none - co-chair
  • none - co-chair
  • none - co-chair
  • none - co-chair
  • Yo-Ping Huang - co-chair
  • Files indicate in-campus access at 5 years and off-campus not accessible
    Date of Defense 2007-06-19 Date of Submission 2007-07-31


    Browse | Search All Available ETDs